Machine learning-based data object storage

ABSTRACT

An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/776,292, entitled “MACHINE LEARNING-BASED DATA OBJECT STORAGE” andfiled on Jan. 29, 2020, which is a continuation of U.S. patentapplication Ser. No. 15/896,943, entitled “MACHINE LEARNING-BASED DATAOBJECT STORAGE” and filed on Feb. 14, 2018, now issued as U.S. Pat. No.10,592,145, which are hereby incorporated by reference herein in theirentireties. Any and all applications, if any, for which a foreign ordomestic priority claim is identified in the Application Data Sheet ofthe present application are hereby incorporated by reference in theirentireties under 37 CFR 1.57.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentand/or the patent disclosure as it appears in the United States Patentand Trademark Office patent file and/or records, but otherwise reservesall copyrights whatsoever.

BACKGROUND

Businesses recognize the commercial value of their data and seekreliable, cost-effective ways to protect the information stored on theircomputer networks while minimizing impact on productivity. A companymight back up critical computing systems such as databases, fileservers, web servers, virtual machines, and so on as part of a daily,weekly, or monthly maintenance schedule. The company may similarlyprotect computing systems used by its employees, such as those used byan accounting department, marketing department, engineering department,and so forth. Given the rapidly expanding volume of data undermanagement, companies also continue to seek innovative techniques formanaging data growth, for example by migrating data to lower-coststorage over time, reducing redundant data, pruning lower priority data,etc. Enterprises also increasingly view their stored data as a valuableasset and look for solutions that leverage their data. For instance,data analysis capabilities, information management, improved datapresentation and access features, and the like, are in increasingdemand.

SUMMARY

Described herein is an information management system that uses machinelearning to predict what data to store in a secondary storage deviceand/or when to perform the storing and/or what data to recall from thesecondary storage device and/or when to perform the recall. For example,a client computing device can be initially configured to store data in asecondary storage device according to one or more storage policies. Amedia agent in the information management system can monitor data usageon the client computing device, using the monitored data usage data totrain a data storage machine learning model. The data storage machinelearning model may be trained such that the model, given an input suchas time, an identification of data object(s), data object metadata,etc., predicts what data to store in a secondary storage device and/orwhen to perform the storing. Once a sufficient amount of monitored datausage data has been obtained and used to train the data storage machinelearning model, the client computing device can be configured to use thetrained data storage machine learning model in place of the storagepolic(ies) to determine which data to store in a secondary storagedevice and/or when to perform the storing.

Similarly, the media agent can generate and store context informationwhen data recall requests are received from a client computing device,using the context information and/or the monitored data usage data totrain a recall machine learning model. The recall machine learning modelmay be trained such that the model, given an input such as time, anidentification of a recalled data object, data object metadata, etc.,predicts what data to recall and/or when to perform the recall. Themedia agent and/or the client computing device can then be configured touse the trained recall machine learning model to determine which data torecall and/or when to perform the recall.

One aspect of the disclosure provides a networked information managementsystem comprising a client computing device having one or more firsthardware processors, where the client computing device is configuredwith first computer-executable instructions that, when executed, causethe client computing device to store one or more data objects in asecondary storage device according to a storage policy at a first time.The networked information management system further comprises one ormore computing devices in communication with the client computingdevice, where the one or more computing devices each have one or moresecond hardware processors, where the one or more computing devices areconfigured with second computer-executable instructions that, whenexecuted, cause the one or more computing devices to: retrieve dataobject usage data associated with the client computing device; train adata storage machine learning (ML) model using the data object usagedata; and transmit the data storage ML model to the client computingdevice such that the client computing device uses the data storage MLmodel instead of the storage policy to determine which of the one ormore data objects to store in the secondary storage device at a secondtime after the first time.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thesecond computer-executable instructions, when executed, further causethe one or more computing devices to: retrieve user directoryinformation from a user directory system, where the user directoryinformation comprises an indication of an active or inactive status ofone or more user credentials, and train the data storage ML model usingthe data object usage data and the user directory information; where thesecond computer-executable instructions, when executed, further causethe one or more computing devices to: retrieve second data object usagedata associated with the client computing device, retrieve the datastorage ML model, retrain the data storage ML model using the seconddata object usage data, and transmit the retrained data storage ML modelto the client computing device such that the client computing deviceuses the retrained data storage ML model instead of the data storage MLmodel to determine which of the one or more data objects to store in thesecondary storage device at a third time after the second time; wherethe second computer-executable instructions, when executed, furthercause the one or more computing devices to: obtain a request to store inthe secondary storage device a first data object in the one or more dataobjects from the client computing device, where the client computingdevice generates the request in response to a prediction produced by thedata storage ML model, process the first data object to form a secondarycopy of the first data object, and store the secondary copy of the firstdata object in the secondary storage device; where the data storage MLmodel generates a prediction, with an associated confidence level,identifying a first data object in the one or more data objects to storein the secondary storage device at the second time after the first timein response to one or more inputs; where the one or more inputs compriseat least one of a current time, an identification of a second dataobject in the one or more data objects generated by a first applicationrunning on the client computing device, an age of the second dataobject, a name of the second data object, a size of the second dataobject, a data object type of the second data object, or informationidentifying an active or inactive status of one or more usercredentials; where the second computer-executable instructions, whenexecuted, further cause the one or more computing devices to train thedata storage ML model by deriving patterns from the data object usagedata; where the data object usage data comprises at least one of dataobject access times, data object permissions, data object ownershipinformation, data object datapath information, information indicatingwhich application running on the client computing device generated adata object, data object size, data object type, or data object nameinformation; and where the one or more data objects comprise at leastone of a file, a folder, a directory, a file system volume, a datablock, or an extent.

Another aspect of the disclosure provides a computer-implementedcomprising: retrieving data object usage data associated with a clientcomputing device, the client computing device having one or more firsthardware processors, where the client computing device is configuredwith computer-executable instructions that, when executed, cause theclient computing device to store one or more data objects in a secondarystorage device according to a storage policy at a first time; training adata storage machine learning (ML) model using the data object usagedata; and transmitting the data storage ML model to the client computingdevice such that the client computing device uses the data storage MLmodel instead of the storage policy to determine which of the one ormore data objects to store in the secondary storage device at a secondtime after the first time.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where training a datastorage ML model further comprises: retrieving user directoryinformation from a user directory system, where the user directoryinformation comprises an indication of an active or inactive status ofone or more user credentials, and training the data storage ML modelusing the data object usage data and the user directory information;where the computer-implemented method further comprises retrievingsecond data object usage data associated with the client computingdevice, retrieving the data storage ML model, retraining the datastorage ML model using the second data object usage data, andtransmitting the retrained data storage ML model to the client computingdevice such that the client computing device uses the retrained datastorage ML model instead of the data storage ML model to determine whichof the one or more data objects to store in the secondary storage deviceat a third time after the second time; where the computer-implementedmethod further comprises: receiving a request to store in the secondarystorage device a first data object in the one or more data objects fromthe client computing device, where the client computing device generatesthe request in response to a prediction produced by the data storage MLmodel, processing the first data object to form a secondary copy of thefirst data object, and storing the secondary copy of the first dataobject in the secondary storage device; where the data storage ML modelgenerates a prediction, with an associated confidence level, identifyinga first data object in the one or more data objects to store in thesecondary storage device at the second time after the first time inresponse to one or more inputs; where the one or more inputs comprise atleast one of a current time, an identification of a second data objectin the one or more data objects generated by a first application runningon the client computing device, an age of the second data object, a nameof the second data object, a size of the second data object, a dataobject type of the second data object, or information identifying anactive or inactive status of one or more user credentials; wheretraining a data storage ML model further comprises training the datastorage ML model by deriving patterns from the data object usage data;where the data object usage data comprises at least one of data objectaccess times, data object permissions, data object ownershipinformation, data object datapath information, information indicatingwhich application running on the client computing device generated adata object, data object size, data object type, or data object nameinformation; and where the one or more data objects comprise at leastone of a file, a folder, a directory, a file system volume, a datablock, or an extent.

Another aspect of the disclosure provides a networked informationmanagement system comprising a client computing device having one ormore first hardware processors, where the client computing device isconfigured with first computer-executable instructions that, whenexecuted, cause the client computing device to store one or more dataobjects in a secondary storage device according to a storage policy at afirst time. The networked information management system furthercomprises one or more computing devices in communication with the clientcomputing device, where the one or more computing devices each have oneor more second hardware processors, where the one or more computingdevices are configured with second computer-executable instructionsthat, when executed, cause the one or more computing devices to:retrieve data object usage data associated with the client computingdevice; train a data storage machine learning (ML) model using the dataobject usage data; and transmit the data storage ML model to the clientcomputing device such that the client computing device uses at least oneof the data storage ML model or the storage policy to determine which ofthe one or more data objects to store in the secondary storage device ata second time after the first time.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thesecond computer-executable instructions, when executed, further causethe one or more computing devices to: retrieve second data object usagedata associated with the client computing device, retrieve the datastorage ML model, retrain the data storage ML model using the seconddata object usage data, and transmit the retrained data storage ML modelto the client computing device such that the client computing deviceuses at least one of the retrained data storage ML model or the storagepolicy instead of the data storage ML model to determine which of theone or more data objects to store in the secondary storage device at athird time after the second time.

Another aspect of the disclosure provides a networked informationmanagement system comprising a client computing device having one ormore first hardware processors, the client computing device configuredto execute a first application that generated one or more data objects.The networked information management system further comprises one ormore computing devices in communication with the client computingdevice, where the one or more computing devices each have one or moresecond hardware processors, where the one or more computing devices areconfigured with computer-executable instructions that, when executed,cause the one or more computing devices to: retrieve data object usagerecall context information associated with the client computing device;train a recall machine learning (ML) model using the data object usagerecall context information; and transmit the recall ML model to theclient computing device such that the client computing device uses therecall ML model to determine which of the one or more data objects torecall from a secondary storage device at a first time.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to: retrieve data object usage data, and trainthe recall ML model using the data object usage recall contextinformation and the data object usage data; where the data object usagedata comprises at least one of data object access times, data objectpermissions, data object ownership information, data object datapathinformation, information indicating which application running on theclient computing device generated a data object, data object size, dataobject type, or data object name information; where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to: retrieve second data object usage recallcontext information associated with the client computing device,retrieve the recall ML model, retrain the recall ML model using thesecond data object usage recall context information, and transmit theretrained recall ML model to the client computing device such that theclient computing device uses the retrained recall ML model instead ofthe recall ML model to determine which of the one or more data objectsto recall at a second time after the first time; where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to: obtain a request to recall a first dataobject in the one or more data objects from the client computing device,where the client computing device generates the request in response to aprediction produced by the recall ML model, retrieve a secondary copy ofthe first data object from the secondary storage device, process thesecondary copy of the first data object to form a primary copy of thefirst data object, and transmit the primary copy of the first dataobject to the client computing device; where the recall ML modelgenerates a prediction, with an associated confidence level, identifyinga first data object in the one or more data objects to recall at thefirst time in response to one or more inputs; where the one or moreinputs comprise at least one of a current time, an identification of areception of a request to recall a second data object in the one or moredata objects that is co-located with the first data object, anidentification of a third data object in the one or more data objectsgenerated by the first application, an age of the third data object, aname of the third data object, a size of the third data object, a dataobject type of the third data object, or a datapath of the third dataobject; where the computer-executable instructions, when executed,further cause the one or more computing devices to train the recall MLmodel by deriving patterns from the data object usage recall contextinformation; where the data object usage recall context informationcomprises at least one of a time that a recall for a first data objectin the one or more data objects was requested, other data objects in theone or more data objects co-located with the requested first dataobject, or a datapath in a file system of the client computing device towhich the requested first data object will be stored; and where the oneor more data objects comprise at least one of a file, a folder, adirectory, a file system volume, a data block, or an extent.

Another aspect of the disclosure provides a computer-implemented methodcomprising: retrieving, by one or more computing devices configured tomanage transmission of data between a client computing device and asecondary storage device, data object usage recall context informationassociated with the client computing device, the client computing deviceconfigured to execute a first application that generated one or moredata objects; training a recall machine learning (ML) model using thedata object usage recall context information; and transmitting therecall ML model to the client computing device such that the clientcomputing device uses the recall ML model to determine which of the oneor more data objects to recall from the secondary storage device at afirst time.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where training a recallML model further comprises: retrieving data object usage data, andtraining the recall ML model using the data object usage recall contextinformation and the data object usage data; where the data object usagedata comprises at least one of data object access times, data objectpermissions, data object ownership information, data object datapathinformation, information indicating which application running on theclient computing device generated a data object, data object size, dataobject type, or data object name information; where thecomputer-implemented method further comprises: retrieving second dataobject usage recall context information associated with the clientcomputing device, retrieving the recall ML model, retraining the recallML model using the second data object usage recall context information,and transmitting the retrained recall ML model to the client computingdevice such that the client computing device uses the retrained recallML model instead of the recall ML model to determine which of the one ormore data objects to recall at a second time after the first time; wherethe computer-implemented method further comprises: obtaining a requestto recall a first data object in the one or more data objects from theclient computing device, where the client computing device generates therequest in response to a prediction produced by the recall ML model,retrieving a secondary copy of the first data object from the secondarystorage device, processing the secondary copy of the first data objectto form a primary copy of the first data object, and transmitting theprimary copy of the first data object to the client computing device;where the recall ML model generates a prediction, with an associatedconfidence level, identifying a first data object in the one or moredata objects to recall at the first time in response to one or moreinputs; where the one or more inputs comprise at least one of a currenttime, an identification of a reception of a request to recall a seconddata object in the one or more data objects that is co-located with thefirst data object, an identification of a third data object in the oneor more data objects generated by the first application, an age of thethird data object, a name of the third data object, a size of the thirddata object, a data object type of the third data object, or a datapathof the third data object; and where training the recall ML model furthercomprises training the recall ML model by deriving patterns from thedata object usage recall context information.

Another aspect of the disclosure provides a networked informationmanagement system comprising a client computing device having one ormore first hardware processors, the client computing device configuredto execute a first application that generated one or more data objects.The networked information management system further comprises one ormore computing devices in communication with the client computingdevice, where the one or more computing devices each have one or moresecond hardware processors, where the one or more computing devices areconfigured with computer-executable instructions that, when executed,cause the one or more computing devices to: retrieve data object usagerecall context information associated with the client computing device;train a recall machine learning (ML) model using the data object usagerecall context information; identify a first data object in the one ormore data objects to recall from a secondary storage device at a firsttime using the recall ML model; retrieve a secondary copy of the firstdata object from the secondary storage device; where process thesecondary copy of the first data object to form a primary copy of thefirst data object; and where transmit the primary copy of the first dataobject to the client computing device at the first time.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to: retrieve second data object usage recallcontext information associated with the client computing device,retrieve the recall ML model, retrain the recall ML model using thesecond data object usage recall context information, and identify asecond data object in the one or more data objects to recall from thesecondary storage device at a second time after the first time using theretrained recall ML model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating an exemplary informationmanagement system.

FIG. 1B is a detailed view of a primary storage device, a secondarystorage device, and some examples of primary data and secondary copydata.

FIG. 1C is a block diagram of an exemplary information management systemincluding a storage manager, one or more data agents, and one or moremedia agents.

FIG. 1D is a block diagram illustrating a scalable informationmanagement system.

FIG. 1E illustrates certain secondary copy operations according to anexemplary storage policy.

FIGS. 1F-1H are block diagrams illustrating suitable data structuresthat may be employed by the information management system.

FIG. 2A illustrates a system and technique for synchronizing primarydata to a destination such as a failover site using secondary copy data.

FIG. 2B illustrates an information management system architectureincorporating use of a network file system (NFS) protocol forcommunicating between the primary and secondary storage subsystems.

FIG. 2C is a block diagram of an example of a highly scalable manageddata pool architecture.

FIG. 3 is a block diagram illustrating some salient portions of aninformation management system that stores and/or recalls data objectsusing machine learning, according to an illustrative embodiment of thepresent invention.

FIG. 4A illustrates a block diagram showing the operations performed tostore a data object in a secondary storage device according to a storagepolicy.

FIG. 4B illustrates a block diagram showing the operations performed totrain a data storage ML model for determining which data objects tostore in a secondary storage device and/or when to perform the storing.

FIG. 4C illustrates a block diagram showing the operations performed tostore a data object in a secondary storage device according to a datastorage ML model.

FIG. 4D illustrates another block diagram showing the operationsperformed to store a data object in a secondary storage device accordingto a data storage ML model.

FIG. 5A illustrates a block diagram showing the operations performed torecall a data object in response to a user request.

FIG. 5B illustrates a block diagram showing the operations performed totrain a recall ML model for determining which data objects to recalland/or when to perform the recall.

FIG. 5C illustrates a block diagram showing the operations performed torecall a data object according to a recall ML model.

FIG. 5D illustrates another block diagram showing the operationsperformed to recall a data object according to a recall ML model.

FIG. 6 depicts some salient operations of a method for training a datastorage ML model according to an illustrative embodiment of the presentinvention.

FIG. 7 depicts some salient operations of a method for training andusing a data storage ML model according to an illustrative embodiment ofthe present invention.

FIG. 8 depicts some salient operations of a method for training a recallML model according to an illustrative embodiment of the presentinvention.

FIG. 9 depicts some salient operations of a method for training andusing a recall ML model according to an illustrative embodiment of thepresent invention.

DETAILED DESCRIPTION

As described herein, data accessed by a client computing device can bestored in a primary storage device. Some data, however, may not beaccessed as frequently as other data. Because some data may not beaccessed as frequently, this data can be transferred away from theprimary storage device in order to increase the amount of memoryavailable for other, more frequently accessed data without significantlydegrading the user experience. Thus, a typical information managementsystem can use one or more storage policies to determine which data totransfer from a primary storage device to a secondary storage device.For example, a client computing device can be configured with one ormore storage policies. A storage policy generally comprises a datastructure or other information source that defines (or includesinformation sufficient to determine) a set of preferences or othercriteria for performing the data transfer. Storage policies can includeone or more of the following: (1) what data will be associated with thestorage policy; (2) a destination to which the data will be stored; (3)datapath information specifying how the data will be communicated to thedestination; (4) the type of secondary copy operation to be performed;and (5) retention information specifying how long the data will beretained at the destination.

Storage policies, however, are static policies that may not adapt to thespecific circumstances in which data is generated and used. For example,a storage policy may define that data not accessed for 90 days should bestored (e.g., backed up, archived, and/or other data protectionoperations) in a secondary storage device. While some data not accessedfor 90 days may be unimportant to a user, other data not accessed forthis time period may be important to a user and the user may otherwiseprefer that the data not be stored in the secondary storage device. Ifthe storage in the secondary storage device occurs and the usersubsequently requests access to the data, then the client computingdevice may provide a delayed response (e.g., as compared to if the datahad not been stored in the secondary storage device) because the clientcomputing device may first have to recall the stored data from asecondary storage device.

Furthermore, the time period defined in a storage policy may not alwaysbe sufficient to properly clear enough memory space to allow for thestorage of newly generated data. For example, the client computingdevice may generate a larger amount of data in some time periods than inother time periods. A wider variety of data may also be accessed morefrequently during some time periods than in other time periods.Accordingly, storing data in a secondary storage device according to thestorage policy may clear a sufficient amount of memory space during sometime periods, but not during other time periods.

Not only do typical information management systems experience reducedperformance due to the implementation of storage policies, but typicalinformation management systems also experience reduced performance basedon the manner in which data recalls are handled. For example, clientcomputing devices are generally not configured with any policies thatdefine what stored data to recall and/or when to perform the recallbecause it can be difficult to determine when a user may request accessto specific set of stored data. Rather, when data is stored, a clientcomputing device recalls the stored data in response to a userrequesting access to the data. As explained above, the process ofrecalling requested data from a secondary storage device can result inthe client computing device providing a delayed response (e.g., a delayof a user-noticeable amount of time, such as seconds or minutes, toretrieve and present the requested data to the user in a userinterface). The delay may be exacerbated if the user requests access toa large amount of stored data (e.g., all of the files in a 1 GB backedup or archived folder).

Accordingly, described herein is an information management system thatuses machine learning to predict what data to store in a secondarystorage device and/or when to perform the storing and/or what data torecall from a secondary storage device and/or when to perform therecall. For example, a client computing device can be initiallyconfigured to store data in a secondary storage device according to oneor more storage policies. A media agent in the information managementsystem can monitor data usage on the client computing device, using themonitored data usage data to train a data storage machine learningmodel. The data storage machine learning model may be trained such thatthe model, given an input such as time, an identification of dataobject(s), data object metadata, etc., predicts what data to store in asecondary storage device and/or when to perform the storing. Once asufficient amount of monitored data usage data has been obtained andused to train the data storage machine learning model, the clientcomputing device can be configured to use the trained data storagemachine learning model in place of the storage polic(ies) to determinewhich data to store in a secondary storage device and/or when to performthe storing.

Similarly, the media agent can generate and store context informationwhen data recall requests are received from a client computing device,using the context information and/or the monitored data usage data totrain a recall machine learning model. The recall machine learning modelmay be trained such that the model, given an input such as time, anidentification of a recalled data object, data object metadata, etc.,predicts what data to recall and/or when to perform the recall. Themedia agent and/or the client computing device can then be configured touse the trained recall machine learning model to determine which data torecall and/or when to perform the recall.

By using machine learning, the information management system can improvethe operational speed of client computing devices by more judiciouslydeciding what data to store in a secondary storage device (e.g., andthereby reducing the likelihood that desired data is inadvertentlystored in a secondary storage device) and/or by proactively recallingcertain data before receiving a user request for such data. Usingmachine learning can also result in the information management systemreducing memory usage in a manner that reduces the likelihood that thememory storage capacity is reached. For example, the data storage and/orrecall machine learning models may cause the client computing deviceand/or the media agent to store and/or recall data in a manner thatreduces the likelihood that either not enough data is stored in thesecondary storage device or too much data is recalled, which can bothresult in a memory storage device reaching a storage capacity.

Detailed descriptions and examples of systems and methods according toone or more illustrative embodiments of the present invention may befound in the section entitled Machine Learning-Based Data Object Storingand Retrieval, as well as in the section entitled Example Embodiments,and also in FIGS. 3 through 8 herein. Furthermore, components andfunctionality for the machine learning-based data object storing andretrieval may be configured and/or incorporated into informationmanagement systems such as those described herein in FIGS. 1A-1H and2A-2C.

Various embodiments described herein are intimately tied to, enabled by,and would not exist except for, computer technology. For example, themachine learning-based data object storing and retrieval describedherein in reference to various embodiments cannot reasonably beperformed by humans alone, without the computer technology upon whichthey are implemented.

Information Management System Overview

With the increasing importance of protecting and leveraging data,organizations simply cannot risk losing critical data. Moreover, runawaydata growth and other modern realities make protecting and managing dataincreasingly difficult. There is therefore a need for efficient,powerful, and user-friendly solutions for protecting and managing dataand for smart and efficient management of data storage. Depending on thesize of the organization, there may be many data production sourceswhich are under the purview of tens, hundreds, or even thousands ofindividuals. In the past, individuals were sometimes responsible formanaging and protecting their own data, and a patchwork of hardware andsoftware point solutions may have been used in any given organization.These solutions were often provided by different vendors and had limitedor no interoperability. Certain embodiments described herein addressthese and other shortcomings of prior approaches by implementingscalable, unified, organization-wide information management, includingdata storage management.

FIG. 1A shows one such information management system 100 (or “system100”), which generally includes combinations of hardware and softwareconfigured to protect and manage data and metadata that are generatedand used by computing devices in system 100. System 100 may be referredto in some embodiments as a “storage management system” or a “datastorage management system.” System 100 performs information managementoperations, some of which may be referred to as “storage operations” or“data storage operations,” to protect and manage the data residing inand/or managed by system 100. The organization that employs system 100may be a corporation or other business entity, non-profit organization,educational institution, household, governmental agency, or the like.

Generally, the systems and associated components described herein may becompatible with and/or provide some or all of the functionality of thesystems and corresponding components described in one or more of thefollowing U.S. patents/publications and patent applications assigned toCommvault Systems, Inc., each of which is hereby incorporated byreference in its entirety herein:

-   -   U.S. Pat. No. 7,035,880, entitled “Modular Backup and Retrieval        System Used in Conjunction With a Storage Area Network”;    -   U.S. Pat. No. 7,107,298, entitled “System And Method For        Archiving Objects In An Information Store”;    -   U.S. Pat. No. 7,246,207, entitled “System and Method for        Dynamically Performing Storage Operations in a Computer        Network”;    -   U.S. Pat. No. 7,315,923, entitled “System And Method For        Combining Data Streams In Pipelined Storage Operations In A        Storage Network”;    -   U.S. Pat. No. 7,343,453, entitled “Hierarchical Systems and        Methods for Providing a Unified View of Storage Information”;    -   U.S. Pat. No. 7,395,282, entitled “Hierarchical Backup and        Retrieval System”;    -   U.S. Pat. No. 7,529,782, entitled “System and Methods for        Performing a Snapshot and for Restoring Data”;    -   U.S. Pat. No. 7,617,262, entitled “System and Methods for        Monitoring Application Data in a Data Replication System”;    -   U.S. Pat. No. 7,734,669, entitled “Managing Copies Of Data”;    -   U.S. Pat. No. 7,747,579, entitled “Metabase for Facilitating        Data Classification”;    -   U.S. Pat. No. 8,156,086, entitled “Systems And Methods For        Stored Data Verification”;    -   U.S. Pat. No. 8,170,995, entitled “Method and System for Offline        Indexing of Content and Classifying Stored Data”;    -   U.S. Pat. No. 8,230,195, entitled “System And Method For        Performing Auxiliary Storage Operations”;    -   U.S. Pat. No. 8,285,681, entitled “Data Object Store and Server        for a Cloud Storage Environment, Including Data Deduplication        and Data Management Across Multiple Cloud Storage Sites”;    -   U.S. Pat. No. 8,307,177, entitled “Systems And Methods For        Management Of Virtualization Data”;    -   U.S. Pat. No. 8,364,652, entitled “Content-Aligned, Block-Based        Deduplication”;    -   U.S. Pat. No. 8,578,120, entitled “Block-Level Single        Instancing”;    -   U.S. Pat. No. 8,954,446, entitled “Client-Side Repository in a        Networked Deduplicated Storage System”;    -   U.S. Pat. No. 9,020,900, entitled “Distributed Deduplicated        Storage System”;    -   U.S. Pat. No. 9,098,495, entitled “Application-Aware and Remote        Single Instance Data Management”;    -   U.S. Pat. No. 9,239,687, entitled “Systems and Methods for        Retaining and Using Data Block Signatures in Data Protection        Operations”;    -   U.S. Pat. Pub. No. 2006/0224846, entitled “System and Method to        Support Single Instance Storage Operations”;    -   U.S. Pat. Pub. No. 2014/0201170, entitled “High Availability        Distributed Deduplicated Storage System”;    -   U.S. patent application Ser. No. 14/721,971, entitled        “Replication Using Deduplicated Secondary Copy Data” (applicant        docket no. 100.422.US1.145; attorney docket no. COMMV.252A);    -   U.S. Patent Application No. 62/265,339 entitled “Live        Synchronization and Management of Virtual Machines across        Computing and Virtualization Platforms and Using Live        Synchronization to Support Disaster Recovery” (applicant docket        no. 100.487.USP1.160; attorney docket no. COMMV.277PR);    -   U.S. Patent Application No. 62/273,286 entitled “Redundant and        Robust Distributed Deduplication Data Storage System” (applicant        docket no. 100.489.USP1.135; attorney docket no. COMMV.279PR);    -   U.S. Patent Application No. 62/294,920, entitled “Data        Protection Operations Based on Network Path Information”        (applicant docket no. 100.497.USP1.105; attorney docket no.        COMMV.283PR);    -   U.S. Patent Application No. 62/297,057, entitled “Data        Restoration Operations Based on Network Path Information”        (applicant docket no. 100.498.USP1.105; attorney docket no.        COMMV.284PR); and    -   U.S. Patent Application No. 62/387,384, entitled        “Application-Level Live Synchronization Across Computing        Platforms Including Synchronizing Co-Resident Applications To        Disparate Standby Destinations And Selectively Synchronizing        Some Applications And Not Others” (applicant docket no.        100.500.USP1.105; attorney docket no. COMMV.286PR).

System 100 includes computing devices and computing technologies. Forinstance, system 100 can include one or more client computing devices102 and secondary storage computing devices 106, as well as storagemanager 140 or a host computing device for it. Computing devices caninclude, without limitation, one or more: workstations, personalcomputers, desktop computers, or other types of generally fixedcomputing systems such as mainframe computers, servers, andminicomputers. Other computing devices can include mobile or portablecomputing devices, such as one or more laptops, tablet computers,personal data assistants, mobile phones (such as smartphones), and othermobile or portable computing devices such as embedded computers, set topboxes, vehicle-mounted devices, wearable computers, etc. Servers caninclude mail servers, file servers, database servers, virtual machineservers, and web servers. Any given computing device comprises one ormore processors (e.g., CPU and/or single-core or multi-core processors),as well as corresponding non-transitory computer memory (e.g.,random-access memory (RAM)) for storing computer programs which are tobe executed by the one or more processors. Other computer memory formass storage of data may be packaged/configured with the computingdevice (e.g., an internal hard disk) and/or may be external andaccessible by the computing device (e.g., network-attached storage, astorage array, etc.). In some cases, a computing device includes cloudcomputing resources, which may be implemented as virtual machines. Forinstance, one or more virtual machines may be provided to theorganization by a third-party cloud service vendor.

In some embodiments, computing devices can include one or more virtualmachine(s) running on a physical host computing device (or “hostmachine”) operated by the organization. As one example, the organizationmay use one virtual machine as a database server and another virtualmachine as a mail server, both virtual machines operating on the samehost machine. A Virtual machine (“VM”) is a software implementation of acomputer that does not physically exist and is instead instantiated inan operating system of a physical computer (or host machine) to enableapplications to execute within the VM's environment, i.e., a VM emulatesa physical computer. A VM includes an operating system and associatedvirtual resources, such as computer memory and processor(s). Ahypervisor operates between the VM and the hardware of the physical hostmachine and is generally responsible for creating and running the VMs.Hypervisors are also known in the art as virtual machine monitors or avirtual machine managers or “VMMs”, and may be implemented in software,firmware, and/or specialized hardware installed on the host machine.Examples of hypervisors include ESX Server, by VMware, Inc. of PaloAlto, Calif.; Microsoft Virtual Server and Microsoft Windows ServerHyper-V, both by Microsoft Corporation of Redmond, Wash.; Sun xVM byOracle America Inc. of Santa Clara, Calif.; and Xen by Citrix Systems,Santa Clara, Calif. The hypervisor provides resources to each virtualoperating system such as a virtual processor, virtual memory, a virtualnetwork device, and a virtual disk. Each virtual machine has one or moreassociated virtual disks. The hypervisor typically stores the data ofvirtual disks in files on the file system of the physical host machine,called virtual machine disk files (“VMDK” in VMware lingo) or virtualhard disk image files (in Microsoft lingo). For example, VMware's ESXServer provides the Virtual Machine File System (VMFS) for the storageof virtual machine disk files. A virtual machine reads data from andwrites data to its virtual disk much the way that a physical machinereads data from and writes data to a physical disk. Examples oftechniques for implementing information management in a cloud computingenvironment are described in U.S. Pat. No. 8,285,681. Examples oftechniques for implementing information management in a virtualizedcomputing environment are described in U.S. Pat. No. 8,307,177.

Information management system 100 can also include electronic datastorage devices, generally used for mass storage of data, including,e.g., primary storage devices 104 and secondary storage devices 108.Storage devices can generally be of any suitable type including, withoutlimitation, disk drives, storage arrays (e.g., storage-area network(SAN) and/or network-attached storage (NAS) technology), semiconductormemory (e.g., solid state storage devices), network attached storage(NAS) devices, tape libraries, or other magnetic, non-tape storagedevices, optical media storage devices, DNA/RNA-based memory technology,combinations of the same, etc. In some embodiments, storage devices formpart of a distributed file system. In some cases, storage devices areprovided in a cloud storage environment (e.g., a private cloud or oneoperated by a third-party vendor), whether for primary data or secondarycopies or both.

Depending on context, the term “information management system” can referto generally all of the illustrated hardware and software components inFIG. 1C, or the term may refer to only a subset of the illustratedcomponents. For instance, in some cases, system 100 generally refers toa combination of specialized components used to protect, move, manage,manipulate, analyze, and/or process data and metadata generated byclient computing devices 102. However, system 100 in some cases does notinclude the underlying components that generate and/or store primarydata 112, such as the client computing devices 102 themselves, and theprimary storage devices 104. Likewise secondary storage devices 108(e.g., a third-party provided cloud storage environment) may not be partof system 100. As an example, “information management system” or“storage management system” may sometimes refer to one or more of thefollowing components, which will be described in further detail below:storage manager, data agent, and media agent.

One or more client computing devices 102 may be part of system 100, eachclient computing device 102 having an operating system and at least oneapplication 110 and one or more accompanying data agents executingthereon; and associated with one or more primary storage devices 104storing primary data 112. Client computing device(s) 102 and primarystorage devices 104 may generally be referred to in some cases asprimary storage subsystem 117.

Client Computing Devices, Clients, and Subclients

Typically, a variety of sources in an organization produce data to beprotected and managed. As just one illustrative example, in a corporateenvironment such data sources can be employee workstations and companyservers such as a mail server, a web server, a database server, atransaction server, or the like. In system 100, data generation sourcesinclude one or more client computing devices 102. A computing devicethat has a data agent 142 installed and operating on it is generallyreferred to as a “client computing device” 102, and may include any typeof computing device, without limitation. A client computing device 102may be associated with one or more users and/or user accounts.

A “client” is a logical component of information management system 100,which may represent a logical grouping of one or more data agentsinstalled on a client computing device 102. Storage manager 140recognizes a client as a component of system 100, and in someembodiments, may automatically create a client component the first timea data agent 142 is installed on a client computing device 102. Becausedata generated by executable component(s) 110 is tracked by theassociated data agent 142 so that it may be properly protected in system100, a client may be said to generate data and to store the generateddata to primary storage, such as primary storage device 104. However,the terms “client” and “client computing device” as used herein do notimply that a client computing device 102 is necessarily configured inthe client/server sense relative to another computing device such as amail server, or that a client computing device 102 cannot be a server inits own right. As just a few examples, a client computing device 102 canbe and/or include mail servers, file servers, database servers, virtualmachine servers, and/or web servers.

Each client computing device 102 may have application(s) 110 executingthereon which generate and manipulate the data that is to be protectedfrom loss and managed in system 100. Applications 110 generallyfacilitate the operations of an organization, and can include, withoutlimitation, mail server applications (e.g., Microsoft Exchange Server),file system applications, mail client applications (e.g., MicrosoftExchange Client), database applications or database management systems(e.g., SQL, Oracle, SAP, Lotus Notes Database), word processingapplications (e.g., Microsoft Word), spreadsheet applications, financialapplications, presentation applications, graphics and/or videoapplications, browser applications, mobile applications, entertainmentapplications, and so on. Each application 110 may be accompanied by anapplication-specific data agent 142, though not all data agents 142 areapplication-specific or associated with only application. A file system,e.g., Microsoft Windows Explorer, may be considered an application 110and may be accompanied by its own data agent 142. Client computingdevices 102 can have at least one operating system (e.g., MicrosoftWindows, Mac OS X, iOS, IBM z/OS, Linux, other Unix-based operatingsystems, etc.) installed thereon, which may support or host one or morefile systems and other applications 110. In some embodiments, a virtualmachine that executes on a host client computing device 102 may beconsidered an application 110 and may be accompanied by a specific dataagent 142 (e.g., virtual server data agent).

Client computing devices 102 and other components in system 100 can beconnected to one another via one or more electronic communicationpathways 114. For example, a first communication pathway 114 maycommunicatively couple client computing device 102 and secondary storagecomputing device 106; a second communication pathway 114 maycommunicatively couple storage manager 140 and client computing device102; and a third communication pathway 114 may communicatively couplestorage manager 140 and secondary storage computing device 106, etc.(see, e.g., FIG. 1A and FIG. 1C). A communication pathway 114 caninclude one or more networks or other connection types including one ormore of the following, without limitation: the Internet, a wide areanetwork (WAN), a local area network (LAN), a Storage Area Network (SAN),a Fibre Channel (FC) connection, a Small Computer System Interface(SCSI) connection, a virtual private network (VPN), a token ring orTCP/IP based network, an intranet network, a point-to-point link, acellular network, a wireless data transmission system, a two-way cablesystem, an interactive kiosk network, a satellite network, a broadbandnetwork, a baseband network, a neural network, a mesh network, an ad hocnetwork, other appropriate computer or telecommunications networks,combinations of the same or the like. Communication pathways 114 in somecases may also include application programming interfaces (APIs)including, e.g., cloud service provider APIs, virtual machine managementAPIs, and hosted service provider APIs. The underlying infrastructure ofcommunication pathways 114 may be wired and/or wireless, analog and/ordigital, or any combination thereof; and the facilities used may beprivate, public, third-party provided, or any combination thereof,without limitation.

A “subclient” is a logical grouping of all or part of a client's primarydata 112. In general, a subclient may be defined according to how thesubclient data is to be protected as a unit in system 100. For example,a subclient may be associated with a certain storage policy. A givenclient may thus comprise several subclients, each subclient associatedwith a different storage policy. For example, some files may form afirst subclient that requires compression and deduplication and isassociated with a first storage policy. Other files of the client mayform a second subclient that requires a different retention schedule aswell as encryption, and may be associated with a different, secondstorage policy. As a result, though the primary data may be generated bythe same application 110 and may belong to one given client, portions ofthe data may be assigned to different subclients for distinct treatmentby system 100. More detail on subclients is given in regard to storagepolicies below.

Primary Data and Exemplary Primary Storage Devices

Primary data 112 is generally production data or “live” data generatedby the operating system and/or applications 110 executing on clientcomputing device 102. Primary data 112 is generally stored on primarystorage device(s) 104 and is organized via a file system operating onthe client computing device 102. Thus, client computing device(s) 102and corresponding applications 110 may create, access, modify, write,delete, and otherwise use primary data 112. Primary data 112 isgenerally in the native format of the source application 110. Primarydata 112 is an initial or first stored body of data generated by thesource application 110. Primary data 112 in some cases is createdsubstantially directly from data generated by the corresponding sourceapplication 110. It can be useful in performing certain tasks toorganize primary data 112 into units of different granularities. Ingeneral, primary data 112 can include files, folders, directories, filesystem volumes, data blocks, extents, or any other hierarchies ororganizations of data objects. As used herein, a “data object” can referto (i) any file that is currently addressable by a file system or thatwas previously addressable by the file system (e.g., an archive file),and/or to (ii) a subset of such a file (e.g., a data block, an extent,etc.). Primary data 112 may include structured data (e.g., databasefiles), unstructured data (e.g., documents), and/or semi-structureddata. See, e.g., FIG. 1B.

It can also be useful in performing certain functions of system 100 toaccess and modify metadata within primary data 112. Metadata generallyincludes information about data objects and/or characteristicsassociated with the data objects. For simplicity herein, it is to beunderstood that, unless expressly stated otherwise, any reference toprimary data 112 generally also includes its associated metadata, butreferences to metadata generally do not include the primary data.Metadata can include, without limitation, one or more of the following:the data owner (e.g., the client or user that generates the data), thelast modified time (e.g., the time of the most recent modification ofthe data object), a data object name (e.g., a file name), a data objectsize (e.g., a number of bytes of data), information about the content(e.g., an indication as to the existence of a particular search term),user-supplied tags, to/from information for email (e.g., an emailsender, recipient, etc.), creation date, file type (e.g., format orapplication type), last accessed time, application type (e.g., type ofapplication that generated the data object), location/network (e.g., acurrent, past or future location of the data object and network pathwaysto/from the data object), geographic location (e.g., GPS coordinates),frequency of change (e.g., a period in which the data object ismodified), business unit (e.g., a group or department that generates,manages or is otherwise associated with the data object), aginginformation (e.g., a schedule, such as a time period, in which the dataobject is migrated to secondary or long term storage), boot sectors,partition layouts, file location within a file folder directorystructure, user permissions, owners, groups, access control lists(ACLs), system metadata (e.g., registry information), combinations ofthe same or other similar information related to the data object. Inaddition to metadata generated by or related to file systems andoperating systems, some applications 110 and/or other components ofsystem 100 maintain indices of metadata for data objects, e.g., metadataassociated with individual email messages. The use of metadata toperform classification and other functions is described in greaterdetail below.

Primary storage devices 104 storing primary data 112 may be relativelyfast and/or expensive technology (e.g., flash storage, a disk drive, ahard-disk storage array, solid state memory, etc.), typically to supporthigh-performance live production environments. Primary data 112 may behighly changeable and/or may be intended for relatively short termretention (e.g., hours, days, or weeks). According to some embodiments,client computing device 102 can access primary data 112 stored inprimary storage device 104 by making conventional file system calls viathe operating system. Each client computing device 102 is generallyassociated with and/or in communication with one or more primary storagedevices 104 storing corresponding primary data 112. A client computingdevice 102 is said to be associated with or in communication with aparticular primary storage device 104 if it is capable of one or moreof: routing and/or storing data (e.g., primary data 112) to the primarystorage device 104, coordinating the routing and/or storing of data tothe primary storage device 104, retrieving data from the primary storagedevice 104, coordinating the retrieval of data from the primary storagedevice 104, and modifying and/or deleting data in the primary storagedevice 104. Thus, a client computing device 102 may be said to accessdata stored in an associated storage device 104.

Primary storage device 104 may be dedicated or shared. In some cases,each primary storage device 104 is dedicated to an associated clientcomputing device 102, e.g., a local disk drive. In other cases, one ormore primary storage devices 104 can be shared by multiple clientcomputing devices 102, e.g., via a local network, in a cloud storageimplementation, etc. As one example, primary storage device 104 can be astorage array shared by a group of client computing devices 102, such asEMC Clariion, EMC Symmetrix, EMC Celerra, Dell EqualLogic, IBM XIV,NetApp FAS, HP EVA, and HP 3PAR.

System 100 may also include hosted services (not shown), which may behosted in some cases by an entity other than the organization thatemploys the other components of system 100. For instance, the hostedservices may be provided by online service providers. Such serviceproviders can provide social networking services, hosted email services,or hosted productivity applications or other hosted applications such assoftware-as-a-service (SaaS), platform-as-a-service (PaaS), applicationservice providers (ASPs), cloud services, or other mechanisms fordelivering functionality via a network. As it services users, eachhosted service may generate additional data and metadata, which may bemanaged by system 100, e.g., as primary data 112. In some cases, thehosted services may be accessed using one of the applications 110. As anexample, a hosted mail service may be accessed via browser running on aclient computing device 102.

Secondary Copies and Exemplary Secondary Storage Devices

Primary data 112 stored on primary storage devices 104 may becompromised in some cases, such as when an employee deliberately oraccidentally deletes or overwrites primary data 112. Or primary storagedevices 104 can be damaged, lost, or otherwise corrupted. For recoveryand/or regulatory compliance purposes, it is therefore useful togenerate and maintain copies of primary data 112. Accordingly, system100 includes one or more secondary storage computing devices 106 and oneor more secondary storage devices 108 configured to create and store oneor more secondary copies 116 of primary data 112 including itsassociated metadata. The secondary storage computing devices 106 and thesecondary storage devices 108 may be referred to as secondary storagesubsystem 118.

Secondary copies 116 can help in search and analysis efforts and meetother information management goals as well, such as: restoring dataand/or metadata if an original version is lost (e.g., by deletion,corruption, or disaster); allowing point-in-time recovery; complyingwith regulatory data retention and electronic discovery (e-discovery)requirements; reducing utilized storage capacity in the productionsystem and/or in secondary storage; facilitating organization and searchof data; improving user access to data files across multiple computingdevices and/or hosted services; and implementing data retention andpruning policies.

A secondary copy 116 can comprise a separate stored copy of data that isderived from one or more earlier-created stored copies (e.g., derivedfrom primary data 112 or from another secondary copy 116). Secondarycopies 116 can include point-in-time data, and may be intended forrelatively long-term retention before some or all of the data is movedto other storage or discarded. In some cases, a secondary copy 116 maybe in a different storage device than other previously stored copies;and/or may be remote from other previously stored copies. Secondarycopies 116 can be stored in the same storage device as primary data 112.For example, a disk array capable of performing hardware snapshotsstores primary data 112 and creates and stores hardware snapshots of theprimary data 112 as secondary copies 116. Secondary copies 116 may bestored in relatively slow and/or lower cost storage (e.g., magnetictape). A secondary copy 116 may be stored in a backup or archive format,or in some other format different from the native source applicationformat or other format of primary data 112.

Secondary storage computing devices 106 may index secondary copies 116(e.g., using a media agent 144), enabling users to browse and restore ata later time and further enabling the lifecycle management of theindexed data. After creation of a secondary copy 116 that representscertain primary data 112, a pointer or other location indicia (e.g., astub) may be placed in primary data 112, or be otherwise associated withprimary data 112, to indicate the current location of a particularsecondary copy 116. Since an instance of a data object or metadata inprimary data 112 may change over time as it is modified by application110 (or hosted service or the operating system), system 100 may createand manage multiple secondary copies 116 of a particular data object ormetadata, each copy representing the state of the data object in primarydata 112 at a particular point in time. Moreover, since an instance of adata object in primary data 112 may eventually be deleted from primarystorage device 104 and the file system, system 100 may continue tomanage point-in-time representations of that data object, even thoughthe instance in primary data 112 no longer exists. For virtual machines,the operating system and other applications 110 of client computingdevice(s) 102 may execute within or under the management ofvirtualization software (e.g., a VMM), and the primary storage device(s)104 may comprise a virtual disk created on a physical storage device.System 100 may create secondary copies 116 of the files or other dataobjects in a virtual disk file and/or secondary copies 116 of the entirevirtual disk file itself (e.g., of an entire .vmdk file).

Secondary copies 116 are distinguishable from corresponding primary data112. First, secondary copies 116 can be stored in a different formatfrom primary data 112 (e.g., backup, archive, or other non-nativeformat). For this or other reasons, secondary copies 116 may not bedirectly usable by applications 110 or client computing device 102(e.g., via standard system calls or otherwise) without modification,processing, or other intervention by system 100 which may be referred toas “restore” operations. Secondary copies 116 may have been processed bydata agent 142 and/or media agent 144 in the course of being created(e.g., compression, deduplication, encryption, integrity markers,indexing, formatting, application-aware metadata, etc.), and thussecondary copy 116 may represent source primary data 112 withoutnecessarily being exactly identical to the source.

Second, secondary copies 116 may be stored on a secondary storage device108 that is inaccessible to application 110 running on client computingdevice 102 and/or hosted service. Some secondary copies 116 may be“offline copies,” in that they are not readily available (e.g., notmounted to tape or disk). Offline copies can include copies of data thatsystem 100 can access without human intervention (e.g., tapes within anautomated tape library, but not yet mounted in a drive), and copies thatthe system 100 can access only with some human intervention (e.g., tapeslocated at an offsite storage site).

Using Intermediate Devices for Creating Secondary Copies—SecondaryStorage Computing Devices

Creating secondary copies can be challenging when hundreds or thousandsof client computing devices 102 continually generate large volumes ofprimary data 112 to be protected. Also, there can be significantoverhead involved in the creation of secondary copies 116. Moreover,specialized programmed intelligence and/or hardware capability isgenerally needed for accessing and interacting with secondary storagedevices 108. Client computing devices 102 may interact directly with asecondary storage device 108 to create secondary copies 116, but in viewof the factors described above, this approach can negatively impact theability of client computing device 102 to serve/service application 110and produce primary data 112. Further, any given client computing device102 may not be optimized for interaction with certain secondary storagedevices 108.

Thus, system 100 may include one or more software and/or hardwarecomponents which generally act as intermediaries between clientcomputing devices 102 (that generate primary data 112) and secondarystorage devices 108 (that store secondary copies 116). In addition tooff-loading certain responsibilities from client computing devices 102,these intermediate components provide other benefits. For instance, asdiscussed further below with respect to FIG. 1D, distributing some ofthe work involved in creating secondary copies 116 can enhancescalability and improve system performance. For instance, usingspecialized secondary storage computing devices 106 and media agents 144for interfacing with secondary storage devices 108 and/or for performingcertain data processing operations can greatly improve the speed withwhich system 100 performs information management operations and can alsoimprove the capacity of the system to handle large numbers of suchoperations, while reducing the computational load on the productionenvironment of client computing devices 102. The intermediate componentscan include one or more secondary storage computing devices 106 as shownin FIG. 1A and/or one or more media agents 144. Media agents arediscussed further below (e.g., with respect to FIGS. 1C-1E). Thesespecial-purpose components of system 100 comprise specialized programmedintelligence and/or hardware capability for writing to, reading from,instructing, communicating with, or otherwise interacting with secondarystorage devices 108.

Secondary storage computing device(s) 106 can comprise any of thecomputing devices described above, without limitation. In some cases,secondary storage computing device(s) 106 also include specializedhardware componentry and/or software intelligence (e.g., specializedinterfaces) for interacting with certain secondary storage device(s) 108with which they may be specially associated.

To create a secondary copy 116 involving the copying of data fromprimary storage subsystem 117 to secondary storage subsystem 118, clientcomputing device 102 may communicate the primary data 112 to be copied(or a processed version thereof generated by a data agent 142) to thedesignated secondary storage computing device 106, via a communicationpathway 114. Secondary storage computing device 106 in turn may furtherprocess and convey the data or a processed version thereof to secondarystorage device 108. One or more secondary copies 116 may be created fromexisting secondary copies 116, such as in the case of an auxiliary copyoperation, described further below.

Exemplary Primary Data and an Exemplary Secondary Copy

FIG. 1B is a detailed view of some specific examples of primary datastored on primary storage device(s) 104 and secondary copy data storedon secondary storage device(s) 108, with other components of the systemremoved for the purposes of illustration. Stored on primary storagedevice(s) 104 are primary data 112 objects including word processingdocuments 119A-B, spreadsheets 120, presentation documents 122, videofiles 124, image files 126, email mailboxes 128 (and corresponding emailmessages 129A-C), HTML/XML or other types of markup language files 130,databases 132 and corresponding tables or other data structures133A-133C. Some or all primary data 112 objects are associated withcorresponding metadata (e.g., “Meta1-11”), which may include file systemmetadata and/or application-specific metadata. Stored on the secondarystorage device(s) 108 are secondary copy 116 data objects 134A-C whichmay include copies of or may otherwise represent corresponding primarydata 112.

Secondary copy data objects 134A-C can individually represent more thanone primary data object. For example, secondary copy data object 134Arepresents three separate primary data objects 133C, 122, and 129C(represented as 133C′, 122′, and 129C′, respectively, and accompanied bycorresponding metadata Meta11, Meta3, and Meta8, respectively).Moreover, as indicated by the prime mark (′), secondary storagecomputing devices 106 or other components in secondary storage subsystem118 may process the data received from primary storage subsystem 117 andstore a secondary copy including a transformed and/or supplementedrepresentation of a primary data object and/or metadata that isdifferent from the original format, e.g., in a compressed, encrypted,deduplicated, or other modified format. For instance, secondary storagecomputing devices 106 can generate new metadata or other informationbased on said processing, and store the newly generated informationalong with the secondary copies. Secondary copy data object 1346represents primary data objects 120, 1336, and 119A as 120′, 133B′, and119A′, respectively, accompanied by corresponding metadata Meta2,Meta10, and Meta1, respectively. Also, secondary copy data object 134Crepresents primary data objects 133A, 1196, and 129A as 133A′, 1196′,and 129A′, respectively, accompanied by corresponding metadata Meta9,Meta5, and Meta6, respectively.

Exemplary Information Management System Architecture

System 100 can incorporate a variety of different hardware and softwarecomponents, which can in turn be organized with respect to one anotherin many different configurations, depending on the embodiment. There arecritical design choices involved in specifying the functionalresponsibilities of the components and the role of each component insystem 100. Such design choices can impact how system 100 performs andadapts to data growth and other changing circumstances. FIG. 1C shows asystem 100 designed according to these considerations and includes:storage manager 140, one or more data agents 142 executing on clientcomputing device(s) 102 and configured to process primary data 112, andone or more media agents 144 executing on one or more secondary storagecomputing devices 106 for performing tasks involving secondary storagedevices 108.

Storage Manager

Storage manager 140 is a centralized storage and/or information managerthat is configured to perform certain control functions and also tostore certain critical information about system 100—hence storagemanager 140 is said to manage system 100. As noted, the number ofcomponents in system 100 and the amount of data under management can belarge. Managing the components and data is therefore a significant task,which can grow unpredictably as the number of components and data scaleto meet the needs of the organization. For these and other reasons,according to certain embodiments, responsibility for controlling system100, or at least a significant portion of that responsibility, isallocated to storage manager 140. Storage manager 140 can be adaptedindependently according to changing circumstances, without having toreplace or re-design the remainder of the system. Moreover, a computingdevice for hosting and/or operating as storage manager 140 can beselected to best suit the functions and networking needs of storagemanager 140. These and other advantages are described in further detailbelow and with respect to FIG. 1D.

Storage manager 140 may be a software module or other application hostedby a suitable computing device. In some embodiments, storage manager 140is itself a computing device that performs the functions describedherein. Storage manager 140 comprises or operates in conjunction withone or more associated data structures such as a dedicated database(e.g., management database 146), depending on the configuration. Thestorage manager 140 generally initiates, performs, coordinates, and/orcontrols storage and other information management operations performedby system 100, e.g., to protect and control primary data 112 andsecondary copies 116. In general, storage manager 140 is said to managesystem 100, which includes communicating with, instructing, andcontrolling in some circumstances components such as data agents 142 andmedia agents 144, etc.

As shown by the dashed arrowed lines 114 in FIG. 1C, storage manager 140may communicate with, instruct, and/or control some or all elements ofsystem 100, such as data agents 142 and media agents 144. In thismanner, storage manager 140 manages the operation of various hardwareand software components in system 100. In certain embodiments, controlinformation originates from storage manager 140 and status as well asindex reporting is transmitted to storage manager 140 by the managedcomponents, whereas payload data and metadata are generally communicatedbetween data agents 142 and media agents 144 (or otherwise betweenclient computing device(s) 102 and secondary storage computing device(s)106), e.g., at the direction of and under the management of storagemanager 140. Control information can generally include parameters andinstructions for carrying out information management operations, suchas, without limitation, instructions to perform a task associated withan operation, timing information specifying when to initiate a task,data path information specifying what components to communicate with oraccess in carrying out an operation, and the like. In other embodiments,some information management operations are controlled or initiated byother components of system 100 (e.g., by media agents 144 or data agents142), instead of or in combination with storage manager 140.

According to certain embodiments, storage manager 140 provides one ormore of the following functions:

-   -   communicating with data agents 142 and media agents 144,        including transmitting instructions, messages, and/or queries,        as well as receiving status reports, index information,        messages, and/or queries, and responding to same;    -   initiating execution of information management operations;    -   initiating restore and recovery operations;    -   managing secondary storage devices 108 and inventory/capacity of        the same;    -   allocating secondary storage devices 108 for secondary copy        operations;    -   reporting, searching, and/or classification of data in system        100;    -   monitoring completion of and status reporting related to        information management operations and jobs;    -   tracking movement of data within system 100;    -   tracking age information relating to secondary copies 116,        secondary storage devices 108, comparing the age information        against retention guidelines, and initiating data pruning when        appropriate;    -   tracking logical associations between components in system 100;    -   protecting metadata associated with system 100, e.g., in        management database 146;    -   implementing job management, schedule management, event        management, alert management, reporting, job history        maintenance, user security management, disaster recovery        management, and/or user interfacing for system administrators        and/or end users of system 100;    -   sending, searching, and/or viewing of log files; and    -   implementing operations management functionality.

Storage manager 140 may maintain an associated database 146 (or “storagemanager database 146” or “management database 146”) ofmanagement-related data and information management policies 148.Database 146 is stored in computer memory accessible by storage manager140. Database 146 may include a management index 150 (or “index 150”) orother data structure(s) that may store: logical associations betweencomponents of the system; user preferences and/or profiles (e.g.,preferences regarding encryption, compression, or deduplication ofprimary data or secondary copies; preferences regarding the scheduling,type, or other aspects of secondary copy or other operations; mappingsof particular information management users or user accounts to certaincomputing devices or other components, etc.; management tasks; mediacontainerization; other useful data; and/or any combination thereof. Forexample, storage manager 140 may use index 150 to track logicalassociations between media agents 144 and secondary storage devices 108and/or movement of data to/from secondary storage devices 108. Forinstance, index 150 may store data associating a client computing device102 with a particular media agent 144 and/or secondary storage device108, as specified in an information management policy 148.

Administrators and others may configure and initiate certain informationmanagement operations on an individual basis. But while this may beacceptable for some recovery operations or other infrequent tasks, it isoften not workable for implementing on-going organization-wide dataprotection and management. Thus, system 100 may utilize informationmanagement policies 148 for specifying and executing informationmanagement operations on an automated basis. Generally, an informationmanagement policy 148 can include a stored data structure or otherinformation source that specifies parameters (e.g., criteria and rules)associated with storage management or other information managementoperations. Storage manager 140 can process an information managementpolicy 148 and/or index 150 and, based on the results, identify aninformation management operation to perform, identify the appropriatecomponents in system 100 to be involved in the operation (e.g., clientcomputing devices 102 and corresponding data agents 142, secondarystorage computing devices 106 and corresponding media agents 144, etc.),establish connections to those components and/or between thosecomponents, and/or instruct and control those components to carry outthe operation. In this manner, system 100 can translate storedinformation into coordinated activity among the various computingdevices in system 100.

Management database 146 may maintain information management policies 148and associated data, although information management policies 148 can bestored in computer memory at any appropriate location outside managementdatabase 146. For instance, an information management policy 148 such asa storage policy may be stored as metadata in a media agent database 152or in a secondary storage device 108 (e.g., as an archive copy) for usein restore or other information management operations, depending on theembodiment. Information management policies 148 are described furtherbelow. According to certain embodiments, management database 146comprises a relational database (e.g., an SQL database) for trackingmetadata, such as metadata associated with secondary copy operations(e.g., what client computing devices 102 and corresponding subclientdata were protected and where the secondary copies are stored and whichmedia agent 144 performed the storage operation(s)). This and othermetadata may additionally be stored in other locations, such as atsecondary storage computing device 106 or on the secondary storagedevice 108, allowing data recovery without the use of storage manager140 in some cases. Thus, management database 146 may comprise dataneeded to kick off secondary copy operations (e.g., storage policies,schedule policies, etc.), status and reporting information aboutcompleted jobs (e.g., status and error reports on yesterday's backupjobs), and additional information sufficient to enable restore anddisaster recovery operations (e.g., media agent associations, locationindexing, content indexing, etc.).

Storage manager 140 may include a jobs agent 156, a user interface 158,and a management agent 154, all of which may be implemented asinterconnected software modules or application programs. These aredescribed further below.

Jobs agent 156 in some embodiments initiates, controls, and/or monitorsthe status of some or all information management operations previouslyperformed, currently being performed, or scheduled to be performed bysystem 100. A job is a logical grouping of information managementoperations such as daily storage operations scheduled for a certain setof subclients (e.g., generating incremental block-level backup copies116 at a certain time every day for database files in a certaingeographical location). Thus, jobs agent 156 may access informationmanagement policies 148 (e.g., in management database 146) to determinewhen, where, and how to initiate/control jobs in system 100.

Storage Manager User Interfaces

User interface 158 may include information processing and displaysoftware, such as a graphical user interface (GUI), an applicationprogram interface (API), and/or other interactive interface(s) throughwhich users and system processes can retrieve information about thestatus of information management operations or issue instructions tostorage manager 140 and other components. Via user interface 158, usersmay issue instructions to the components in system 100 regardingperformance of secondary copy and recovery operations. For example, auser may modify a schedule concerning the number of pending secondarycopy operations. As another example, a user may employ the GUI to viewthe status of pending secondary copy jobs or to monitor the status ofcertain components in system 100 (e.g., the amount of capacity left in astorage device). Storage manager 140 may track information that permitsit to select, designate, or otherwise identify content indices,deduplication databases, or similar databases or resources or data setswithin its information management cell (or another cell) to be searchedin response to certain queries. Such queries may be entered by the userby interacting with user interface 158.

Various embodiments of information management system 100 may beconfigured and/or designed to generate user interface data usable forrendering the various interactive user interfaces described. The userinterface data may be used by system 100 and/or by another system,device, and/or software program (for example, a browser program), torender the interactive user interfaces. The interactive user interfacesmay be displayed on, for example, electronic displays (including, forexample, touch-enabled displays), consoles, etc., whetherdirect-connected to storage manager 140 or communicatively coupledremotely, e.g., via an internet connection. The present disclosuredescribes various embodiments of interactive and dynamic userinterfaces, some of which may be generated by user interface agent 158,and which are the result of significant technological development. Theuser interfaces described herein may provide improved human-computerinteractions, allowing for significant cognitive and ergonomicefficiencies and advantages over previous systems, including reducedmental workloads, improved decision-making, and the like. User interface158 may operate in a single integrated view or console (not shown). Theconsole may support a reporting capability for generating a variety ofreports, which may be tailored to a particular aspect of informationmanagement.

User interfaces are not exclusive to storage manager 140 and in someembodiments a user may access information locally from a computingdevice component of system 100. For example, some information pertainingto installed data agents 142 and associated data streams may beavailable from client computing device 102. Likewise, some informationpertaining to media agents 144 and associated data streams may beavailable from secondary storage computing device 106.

Storage Manager Management Agent

Management agent 154 can provide storage manager 140 with the ability tocommunicate with other components within system 100 and/or with otherinformation management cells via network protocols and applicationprogramming interfaces (APIs) including, e.g., HTTP, HTTPS, FTP, REST,virtualization software APIs, cloud service provider APIs, and hostedservice provider APIs, without limitation. Management agent 154 alsoallows multiple information management cells to communicate with oneanother. For example, system 100 in some cases may be one informationmanagement cell in a network of multiple cells adjacent to one anotheror otherwise logically related, e.g., in a WAN or LAN. With thisarrangement, the cells may communicate with one another throughrespective management agents 154. Inter-cell communications andhierarchy is described in greater detail in e.g., U.S. Pat. No.7,343,453.

Information Management Cell

An “information management cell” (or “storage operation cell” or “cell”)may generally include a logical and/or physical grouping of acombination of hardware and software components associated withperforming information management operations on electronic data,typically one storage manager 140 and at least one data agent 142(executing on a client computing device 102) and at least one mediaagent 144 (executing on a secondary storage computing device 106). Forinstance, the components shown in FIG. 1C may together form aninformation management cell. Thus, in some configurations, a system 100may be referred to as an information management cell or a storageoperation cell. A given cell may be identified by the identity of itsstorage manager 140, which is generally responsible for managing thecell.

Multiple cells may be organized hierarchically, so that cells mayinherit properties from hierarchically superior cells or be controlledby other cells in the hierarchy (automatically or otherwise).Alternatively, in some embodiments, cells may inherit or otherwise beassociated with information management policies, preferences,information management operational parameters, or other properties orcharacteristics according to their relative position in a hierarchy ofcells. Cells may also be organized hierarchically according to function,geography, architectural considerations, or other factors useful ordesirable in performing information management operations. For example,a first cell may represent a geographic segment of an enterprise, suchas a Chicago office, and a second cell may represent a differentgeographic segment, such as a New York City office. Other cells mayrepresent departments within a particular office, e.g., human resources,finance, engineering, etc. Where delineated by function, a first cellmay perform one or more first types of information management operations(e.g., one or more first types of secondary copies at a certainfrequency), and a second cell may perform one or more second types ofinformation management operations (e.g., one or more second types ofsecondary copies at a different frequency and under different retentionrules). In general, the hierarchical information is maintained by one ormore storage managers 140 that manage the respective cells (e.g., incorresponding management database(s) 146).

Data Agents

A variety of different applications 110 can operate on a given clientcomputing device 102, including operating systems, file systems,database applications, e-mail applications, and virtual machines, justto name a few. And, as part of the process of creating and restoringsecondary copies 116, the client computing device 102 may be tasked withprocessing and preparing the primary data 112 generated by these variousapplications 110. Moreover, the nature of the processing/preparation candiffer across application types, e.g., due to inherent structural,state, and formatting differences among applications 110 and/or theoperating system of client computing device 102. Each data agent 142 istherefore advantageously configured in some embodiments to assist in theperformance of information management operations based on the type ofdata that is being protected at a client-specific and/orapplication-specific level.

Data agent 142 is a component of information system 100 and is generallydirected by storage manager 140 to participate in creating or restoringsecondary copies 116. Data agent 142 may be a software program (e.g., inthe form of a set of executable binary files) that executes on the sameclient computing device 102 as the associated application 110 that dataagent 142 is configured to protect. Data agent 142 is generallyresponsible for managing, initiating, or otherwise assisting in theperformance of information management operations in reference to itsassociated application(s) 110 and corresponding primary data 112 whichis generated/accessed by the particular application(s) 110. Forinstance, data agent 142 may take part in copying, archiving, migrating,and/or replicating of certain primary data 112 stored in the primarystorage device(s) 104. Data agent 142 may receive control informationfrom storage manager 140, such as commands to transfer copies of dataobjects and/or metadata to one or more media agents 144. Data agent 142also may compress, deduplicate, and encrypt certain primary data 112, aswell as capture application-related metadata before transmitting theprocessed data to media agent 144. Data agent 142 also may receiveinstructions from storage manager 140 to restore (or assist inrestoring) a secondary copy 116 from secondary storage device 108 toprimary storage 104, such that the restored data may be properlyaccessed by application 110 in a suitable format as though it wereprimary data 112.

Each data agent 142 may be specialized for a particular application 110.For instance, different individual data agents 142 may be designed tohandle Microsoft Exchange data, Lotus Notes data, Microsoft Windows filesystem data, Microsoft Active Directory Objects data, SQL Server data,SharePoint data, Oracle database data, SAP database data, virtualmachines and/or associated data, and other types of data. A file systemdata agent, for example, may handle data files and/or other file systeminformation. If a client computing device 102 has two or more types ofdata 112, a specialized data agent 142 may be used for each data type.For example, to backup, migrate, and/or restore all of the data on aMicrosoft Exchange server, the client computing device 102 may use: (1)a Microsoft Exchange Mailbox data agent 142 to back up the Exchangemailboxes; (2) a Microsoft Exchange Database data agent 142 to back upthe Exchange databases; (3) a Microsoft Exchange Public Folder dataagent 142 to back up the Exchange Public Folders; and (4) a MicrosoftWindows File System data agent 142 to back up the file system of clientcomputing device 102. In this example, these specialized data agents 142are treated as four separate data agents 142 even though they operate onthe same client computing device 102. Other examples may include archivemanagement data agents such as a migration archiver or a compliancearchiver, Quick Recovery® agents, and continuous data replicationagents. Application-specific data agents 142 can provide improvedperformance as compared to generic agents. For instance, becauseapplication-specific data agents 142 may only handle data for a singlesoftware application, the design, operation, and performance of the dataagent 142 can be streamlined. The data agent 142 may therefore executefaster and consume less persistent storage and/or operating memory thandata agents designed to generically accommodate multiple differentsoftware applications 110.

Each data agent 142 may be configured to access data and/or metadatastored in the primary storage device(s) 104 associated with data agent142 and its host client computing device 102, and process the dataappropriately. For example, during a secondary copy operation, dataagent 142 may arrange or assemble the data and metadata into one or morefiles having a certain format (e.g., a particular backup or archiveformat) before transferring the file(s) to a media agent 144 or othercomponent. The file(s) may include a list of files or other metadata. Insome embodiments, a data agent 142 may be distributed between clientcomputing device 102 and storage manager 140 (and any other intermediatecomponents) or may be deployed from a remote location or its functionsapproximated by a remote process that performs some or all of thefunctions of data agent 142. In addition, a data agent 142 may performsome functions provided by media agent 144. Other embodiments may employone or more generic data agents 142 that can handle and process datafrom two or more different applications 110, or that can handle andprocess multiple data types, instead of or in addition to usingspecialized data agents 142. For example, one generic data agent 142 maybe used to back up, migrate and restore Microsoft Exchange Mailbox dataand Microsoft Exchange Database data, while another generic data agentmay handle Microsoft Exchange Public Folder data and Microsoft WindowsFile System data.

Media Agents

As noted, off-loading certain responsibilities from client computingdevices 102 to intermediate components such as secondary storagecomputing device(s) 106 and corresponding media agent(s) 144 can providea number of benefits including improved performance of client computingdevice 102, faster and more reliable information management operations,and enhanced scalability. In one example which will be discussed furtherbelow, media agent 144 can act as a local cache of recently-copied dataand/or metadata stored to secondary storage device(s) 108, thusimproving restore capabilities and performance for the cached data.

Media agent 144 is a component of system 100 and is generally directedby storage manager 140 in creating and restoring secondary copies 116.Whereas storage manager 140 generally manages system 100 as a whole,media agent 144 provides a portal to certain secondary storage devices108, such as by having specialized features for communicating with andaccessing certain associated secondary storage device 108. Media agent144 may be a software program (e.g., in the form of a set of executablebinary files) that executes on a secondary storage computing device 106.Media agent 144 generally manages, coordinates, and facilitates thetransmission of data between a data agent 142 (executing on clientcomputing device 102) and secondary storage device(s) 108 associatedwith media agent 144. For instance, other components in the system mayinteract with media agent 144 to gain access to data stored onassociated secondary storage device(s) 108, (e.g., to browse, read,write, modify, delete, or restore data). Moreover, media agents 144 cangenerate and store information relating to characteristics of the storeddata and/or metadata, or can generate and store other types ofinformation that generally provides insight into the contents of thesecondary storage devices 108—generally referred to as indexing of thestored secondary copies 116. Each media agent 144 may operate on adedicated secondary storage computing device 106, while in otherembodiments a plurality of media agents 144 may operate on the samesecondary storage computing device 106.

A media agent 144 may be associated with a particular secondary storagedevice 108 if that media agent 144 is capable of one or more of: routingand/or storing data to the particular secondary storage device 108;coordinating the routing and/or storing of data to the particularsecondary storage device 108; retrieving data from the particularsecondary storage device 108; coordinating the retrieval of data fromthe particular secondary storage device 108; and modifying and/ordeleting data retrieved from the particular secondary storage device108. Media agent 144 in certain embodiments is physically separate fromthe associated secondary storage device 108. For instance, a media agent144 may operate on a secondary storage computing device 106 in adistinct housing, package, and/or location from the associated secondarystorage device 108. In one example, a media agent 144 operates on afirst server computer and is in communication with a secondary storagedevice(s) 108 operating in a separate rack-mounted RAID-based system.

A media agent 144 associated with a particular secondary storage device108 may instruct secondary storage device 108 to perform an informationmanagement task. For instance, a media agent 144 may instruct a tapelibrary to use a robotic arm or other retrieval means to load or eject acertain storage media, and to subsequently archive, migrate, or retrievedata to or from that media, e.g., for the purpose of restoring data to aclient computing device 102. As another example, a secondary storagedevice 108 may include an array of hard disk drives or solid statedrives organized in a RAID configuration, and media agent 144 mayforward a logical unit number (LUN) and other appropriate information tothe array, which uses the received information to execute the desiredsecondary copy operation. Media agent 144 may communicate with asecondary storage device 108 via a suitable communications link, such asa SCSI or Fibre Channel link.

Each media agent 144 may maintain an associated media agent database152. Media agent database 152 may be stored to a disk or other storagedevice (not shown) that is local to the secondary storage computingdevice 106 on which media agent 144 executes. In other cases, mediaagent database 152 is stored separately from the host secondary storagecomputing device 106. Media agent database 152 can include, among otherthings, a media agent index 153 (see, e.g., FIG. 1C). In some cases,media agent index 153 does not form a part of and is instead separatefrom media agent database 152.

Media agent index 153 (or “index 153”) may be a data structureassociated with the particular media agent 144 that includes informationabout the stored data associated with the particular media agent andwhich may be generated in the course of performing a secondary copyoperation or a restore. Index 153 provides a fast and efficientmechanism for locating/browsing secondary copies 116 or other datastored in secondary storage devices 108 without having to accesssecondary storage device 108 to retrieve the information from there. Forinstance, for each secondary copy 116, index 153 may include metadatasuch as a list of the data objects (e.g., files/subdirectories, databaseobjects, mailbox objects, etc.), a logical path to the secondary copy116 on the corresponding secondary storage device 108, locationinformation (e.g., offsets) indicating where the data objects are storedin the secondary storage device 108, when the data objects were createdor modified, etc. Thus, index 153 includes metadata associated with thesecondary copies 116 that is readily available for use from media agent144. In some embodiments, some or all of the information in index 153may instead or additionally be stored along with secondary copies 116 insecondary storage device 108. In some embodiments, a secondary storagedevice 108 can include sufficient information to enable a “bare metalrestore,” where the operating system and/or software applications of afailed client computing device 102 or another target may beautomatically restored without manually reinstalling individual softwarepackages (including operating systems).

Because index 153 may operate as a cache, it can also be referred to asan “index cache.” In such cases, information stored in index cache 153typically comprises data that reflects certain particulars aboutrelatively recent secondary copy operations. After some triggeringevent, such as after some time elapses or index cache 153 reaches aparticular size, certain portions of index cache 153 may be copied ormigrated to secondary storage device 108, e.g., on a least-recently-usedbasis. This information may be retrieved and uploaded back into indexcache 153 or otherwise restored to media agent 144 to facilitateretrieval of data from the secondary storage device(s) 108. In someembodiments, the cached information may include format orcontainerization information related to archives or other files storedon storage device(s) 108.

In some alternative embodiments media agent 144 generally acts as acoordinator or facilitator of secondary copy operations between clientcomputing devices 102 and secondary storage devices 108, but does notactually write the data to secondary storage device 108. For instance,storage manager 140 (or media agent 144) may instruct a client computingdevice 102 and secondary storage device 108 to communicate with oneanother directly. In such a case, client computing device 102 transmitsdata directly or via one or more intermediary components to secondarystorage device 108 according to the received instructions, and viceversa. Media agent 144 may still receive, process, and/or maintainmetadata related to the secondary copy operations, i.e., may continue tobuild and maintain index 153. In these embodiments, payload data canflow through media agent 144 for the purposes of populating index 153,but not for writing to secondary storage device 108. Media agent 144and/or other components such as storage manager 140 may in some casesincorporate additional functionality, such as data classification,content indexing, deduplication, encryption, compression, and the like.Further details regarding these and other functions are described below.

Distributed, Scalable Architecture

As described, certain functions of system 100 can be distributed amongstvarious physical and/or logical components. For instance, one or more ofstorage manager 140, data agents 142, and media agents 144 may operateon computing devices that are physically separate from one another. Thisarchitecture can provide a number of benefits. For instance, hardwareand software design choices for each distributed component can betargeted to suit its particular function. The secondary computingdevices 106 on which media agents 144 operate can be tailored forinteraction with associated secondary storage devices 108 and providefast index cache operation, among other specific tasks. Similarly,client computing device(s) 102 can be selected to effectively serviceapplications 110 in order to efficiently produce and store primary data112.

Moreover, in some cases, one or more of the individual components ofinformation management system 100 can be distributed to multipleseparate computing devices. As one example, for large file systems wherethe amount of data stored in management database 146 is relativelylarge, database 146 may be migrated to or may otherwise reside on aspecialized database server (e.g., an SQL server) separate from a serverthat implements the other functions of storage manager 140. Thisdistributed configuration can provide added protection because database146 can be protected with standard database utilities (e.g., SQL logshipping or database replication) independent from other functions ofstorage manager 140. Database 146 can be efficiently replicated to aremote site for use in the event of a disaster or other data loss at theprimary site. Or database 146 can be replicated to another computingdevice within the same site, such as to a higher performance machine inthe event that a storage manager host computing device can no longerservice the needs of a growing system 100.

The distributed architecture also provides scalability and efficientcomponent utilization. FIG. 1D shows an embodiment of informationmanagement system 100 including a plurality of client computing devices102 and associated data agents 142 as well as a plurality of secondarystorage computing devices 106 and associated media agents 144.Additional components can be added or subtracted based on the evolvingneeds of system 100. For instance, depending on where bottlenecks areidentified, administrators can add additional client computing devices102, secondary storage computing devices 106, and/or secondary storagedevices 108. Moreover, where multiple fungible components are available,load balancing can be implemented to dynamically address identifiedbottlenecks. As an example, storage manager 140 may dynamically selectwhich media agents 144 and/or secondary storage devices 108 to use forstorage operations based on a processing load analysis of media agents144 and/or secondary storage devices 108, respectively.

Where system 100 includes multiple media agents 144 (see, e.g., FIG.1D), a first media agent 144 may provide failover functionality for asecond failed media agent 144. In addition, media agents 144 can bedynamically selected to provide load balancing. Each client computingdevice 102 can communicate with, among other components, any of themedia agents 144, e.g., as directed by storage manager 140. And eachmedia agent 144 may communicate with, among other components, any ofsecondary storage devices 108, e.g., as directed by storage manager 140.Thus, operations can be routed to secondary storage devices 108 in adynamic and highly flexible manner, to provide load balancing, failover,etc. Further examples of scalable systems capable of dynamic storageoperations, load balancing, and failover are provided in U.S. Pat. No.7,246,207.

While distributing functionality amongst multiple computing devices canhave certain advantages, in other contexts it can be beneficial toconsolidate functionality on the same computing device. In alternativeconfigurations, certain components may reside and execute on the samecomputing device. As such, in other embodiments, one or more of thecomponents shown in FIG. 1C may be implemented on the same computingdevice. In one configuration, a storage manager 140, one or more dataagents 142, and/or one or more media agents 144 are all implemented onthe same computing device. In other embodiments, one or more data agents142 and one or more media agents 144 are implemented on the samecomputing device, while storage manager 140 is implemented on a separatecomputing device, etc. without limitation.

Exemplary Types of Information Management Operations, Including StorageOperations

In order to protect and leverage stored data, system 100 can beconfigured to perform a variety of information management operations,which may also be referred to in some cases as storage managementoperations or storage operations. These operations can generally include(i) data movement operations, (ii) processing and data manipulationoperations, and (iii) analysis, reporting, and management operations.

Data Movement Operations, Including Secondary Copy Operations

Data movement operations are generally storage operations that involvethe copying or migration of data between different locations in system100. For example, data movement operations can include operations inwhich stored data is copied, migrated, or otherwise transferred from oneor more first storage devices to one or more second storage devices,such as from primary storage device(s) 104 to secondary storagedevice(s) 108, from secondary storage device(s) 108 to differentsecondary storage device(s) 108, from secondary storage devices 108 toprimary storage devices 104, or from primary storage device(s) 104 todifferent primary storage device(s) 104, or in some cases within thesame primary storage device 104 such as within a storage array.

Data movement operations can include by way of example, backupoperations, archive operations, information lifecycle managementoperations such as hierarchical storage management operations,replication operations (e.g., continuous data replication), snapshotoperations, deduplication or single-instancing operations, auxiliarycopy operations, disaster-recovery copy operations, and the like. Aswill be discussed, some of these operations do not necessarily createdistinct copies. Nonetheless, some or all of these operations aregenerally referred to as “secondary copy operations” for simplicity,because they involve secondary copies. Data movement also comprisesrestoring secondary copies.

Backup Operations

A backup operation creates a copy of a version of primary data 112 at aparticular point in time (e.g., one or more files or other data units).Each subsequent backup copy 116 (which is a form of secondary copy 116)may be maintained independently of the first. A backup generallyinvolves maintaining a version of the copied primary data 112 as well asbackup copies 116. Further, a backup copy in some embodiments isgenerally stored in a form that is different from the native format,e.g., a backup format. This contrasts to the version in primary data 112which may instead be stored in a format native to the sourceapplication(s) 110. In various cases, backup copies can be stored in aformat in which the data is compressed, encrypted, deduplicated, and/orotherwise modified from the original native application format. Forexample, a backup copy may be stored in a compressed backup format thatfacilitates efficient long-term storage. Backup copies 116 can haverelatively long retention periods as compared to primary data 112, whichis generally highly changeable. Backup copies 116 may be stored on mediawith slower retrieval times than primary storage device 104. Some backupcopies may have shorter retention periods than some other types ofsecondary copies 116, such as archive copies (described below). Backupsmay be stored at an offsite location.

Backup operations can include full backups, differential backups,incremental backups, “synthetic full” backups, and/or creating a“reference copy.” A full backup (or “standard full backup”) in someembodiments is generally a complete image of the data to be protected.However, because full backup copies can consume a relatively largeamount of storage, it can be useful to use a full backup copy as abaseline and only store changes relative to the full backup copyafterwards.

A differential backup operation (or cumulative incremental backupoperation) tracks and stores changes that occurred since the last fullbackup. Differential backups can grow quickly in size, but can restorerelatively efficiently because a restore can be completed in some casesusing only the full backup copy and the latest differential copy.

An incremental backup operation generally tracks and stores changessince the most recent backup copy of any type, which can greatly reducestorage utilization. In some cases, however, restoring can be lengthycompared to full or differential backups because completing a restoreoperation may involve accessing a full backup in addition to multipleincremental backups.

Synthetic full backups generally consolidate data without directlybacking up data from the client computing device. A synthetic fullbackup is created from the most recent full backup (i.e., standard orsynthetic) and subsequent incremental and/or differential backups. Theresulting synthetic full backup is identical to what would have beencreated had the last backup for the subclient been a standard fullbackup. Unlike standard full, incremental, and differential backups,however, a synthetic full backup does not actually transfer data fromprimary storage to the backup media, because it operates as a backupconsolidator. A synthetic full backup extracts the index data of eachparticipating subclient. Using this index data and the previously backedup user data images, it builds new full backup images (e.g., bitmaps),one for each subclient. The new backup images consolidate the index anduser data stored in the related incremental, differential, and previousfull backups into a synthetic backup file that fully represents thesubclient (e.g., via pointers) but does not comprise all its constituentdata.

Any of the above types of backup operations can be at the volume level,file level, or block level. Volume level backup operations generallyinvolve copying of a data volume (e.g., a logical disk or partition) asa whole. In a file-level backup, information management system 100generally tracks changes to individual files and includes copies offiles in the backup copy. For block-level backups, files are broken intoconstituent blocks, and changes are tracked at the block level. Uponrestore, system 100 reassembles the blocks into files in a transparentfashion. Far less data may actually be transferred and copied tosecondary storage devices 108 during a file-level copy than avolume-level copy. Likewise, a block-level copy may transfer less datathan a file-level copy, resulting in faster execution. However,restoring a relatively higher-granularity copy can result in longerrestore times. For instance, when restoring a block-level copy, theprocess of locating and retrieving constituent blocks can sometimes takelonger than restoring file-level backups.

A reference copy may comprise copy(ies) of selected objects from backedup data, typically to help organize data by keeping contextualinformation from multiple sources together, and/or help retain specificdata for a longer period of time, such as for legal hold needs. Areference copy generally maintains data integrity, and when the data isrestored, it may be viewed in the same format as the source data. Insome embodiments, a reference copy is based on a specialized client,individual subclient and associated information management policies(e.g., storage policy, retention policy, etc.) that are administeredwithin system 100.

Archive Operations

Because backup operations generally involve maintaining a version of thecopied primary data 112 and also maintaining backup copies in secondarystorage device(s) 108, they can consume significant storage capacity. Toreduce storage consumption, an archive operation according to certainembodiments creates an archive copy 116 by both copying and removingsource data. Or, seen another way, archive operations can involve movingsome or all of the source data to the archive destination. Thus, datasatisfying criteria for removal (e.g., data of a threshold age or size)may be removed from source storage. The source data may be primary data112 or a secondary copy 116, depending on the situation. As with backupcopies, archive copies can be stored in a format in which the data iscompressed, encrypted, deduplicated, and/or otherwise modified from theformat of the original application or source copy. In addition, archivecopies may be retained for relatively long periods of time (e.g., years)and, in some cases are never deleted. In certain embodiments, archivecopies may be made and kept for extended periods in order to meetcompliance regulations.

Archiving can also serve the purpose of freeing up space in primarystorage device(s) 104 and easing the demand on computational resourceson client computing device 102. Similarly, when a secondary copy 116 isarchived, the archive copy can therefore serve the purpose of freeing upspace in the source secondary storage device(s) 108. Examples of dataarchiving operations are provided in U.S. Pat. No. 7,107,298.

Snapshot Operations

Snapshot operations can provide a relatively lightweight, efficientmechanism for protecting data. From an end-user viewpoint, a snapshotmay be thought of as an “instant” image of primary data 112 at a givenpoint in time, and may include state and/or status information relativeto an application 110 that creates/manages primary data 112. In oneembodiment, a snapshot may generally capture the directory structure ofan object in primary data 112 such as a file or volume or other data setat a particular moment in time and may also preserve file attributes andcontents. A snapshot in some cases is created relatively quickly, e.g.,substantially instantly, using a minimum amount of file space, but maystill function as a conventional file system backup.

A “hardware snapshot” (or “hardware-based snapshot”) operation occurswhere a target storage device (e.g., a primary storage device 104 or asecondary storage device 108) performs the snapshot operation in aself-contained fashion, substantially independently, using hardware,firmware and/or software operating on the storage device itself. Forinstance, the storage device may perform snapshot operations generallywithout intervention or oversight from any of the other components ofthe system 100, e.g., a storage array may generate an “array-created”hardware snapshot and may also manage its storage, integrity,versioning, etc. In this manner, hardware snapshots can off-load othercomponents of system 100 from snapshot processing. An array may receivea request from another component to take a snapshot and then proceed toexecute the “hardware snapshot” operations autonomously, preferablyreporting success to the requesting component.

A “software snapshot” (or “software-based snapshot”) operation, on theother hand, occurs where a component in system 100 (e.g., clientcomputing device 102, etc.) implements a software layer that manages thesnapshot operation via interaction with the target storage device. Forinstance, the component executing the snapshot management software layermay derive a set of pointers and/or data that represents the snapshot.The snapshot management software layer may then transmit the same to thetarget storage device, along with appropriate instructions for writingthe snapshot. One example of a software snapshot product is MicrosoftVolume Snapshot Service (VSS), which is part of the Microsoft Windowsoperating system.

Some types of snapshots do not actually create another physical copy ofall the data as it existed at the particular point in time, but maysimply create pointers that map files and directories to specific memorylocations (e.g., to specific disk blocks) where the data resides as itexisted at the particular point in time. For example, a snapshot copymay include a set of pointers derived from the file system or from anapplication. In some other cases, the snapshot may be created at theblock-level, such that creation of the snapshot occurs without awarenessof the file system. Each pointer points to a respective stored datablock, so that collectively, the set of pointers reflect the storagelocation and state of the data object (e.g., file(s) or volume(s) ordata set(s)) at the point in time when the snapshot copy was created.

An initial snapshot may use only a small amount of disk space needed torecord a mapping or other data structure representing or otherwisetracking the blocks that correspond to the current state of the filesystem. Additional disk space is usually required only when files anddirectories change later on. Furthermore, when files change, typicallyonly the pointers which map to blocks are copied, not the blocksthemselves. For example for “copy-on-write” snapshots, when a blockchanges in primary storage, the block is copied to secondary storage orcached in primary storage before the block is overwritten in primarystorage, and the pointer to that block is changed to reflect the newlocation of that block. The snapshot mapping of file system data mayalso be updated to reflect the changed block(s) at that particular pointin time. In some other cases, a snapshot includes a full physical copyof all or substantially all of the data represented by the snapshot.Further examples of snapshot operations are provided in U.S. Pat. No.7,529,782. A snapshot copy in many cases can be made quickly and withoutsignificantly impacting primary computing resources because largeamounts of data need not be copied or moved. In some embodiments, asnapshot may exist as a virtual file system, parallel to the actual filesystem. Users in some cases gain read-only access to the record of filesand directories of the snapshot. By electing to restore primary data 112from a snapshot taken at a given point in time, users may also returnthe current file system to the state of the file system that existedwhen the snapshot was taken.

Replication Operations

Replication is another type of secondary copy operation. Some types ofsecondary copies 116 periodically capture images of primary data 112 atparticular points in time (e.g., backups, archives, and snapshots).However, it can also be useful for recovery purposes to protect primarydata 112 in a more continuous fashion, by replicating primary data 112substantially as changes occur. In some cases a replication copy can bea mirror copy, for instance, where changes made to primary data 112 aremirrored or substantially immediately copied to another location (e.g.,to secondary storage device(s) 108). By copying each write operation tothe replication copy, two storage systems are kept synchronized orsubstantially synchronized so that they are virtually identical atapproximately the same time. Where entire disk volumes are mirrored,however, mirroring can require significant amount of storage space andutilizes a large amount of processing resources.

According to some embodiments, secondary copy operations are performedon replicated data that represents a recoverable state, or “known goodstate” of a particular application running on the source system. Forinstance, in certain embodiments, known good replication copies may beviewed as copies of primary data 112. This feature allows the system todirectly access, copy, restore, back up, or otherwise manipulate thereplication copies as if they were the “live” primary data 112. This canreduce access time, storage utilization, and impact on sourceapplications 110, among other benefits. Based on known good stateinformation, system 100 can replicate sections of application data thatrepresent a recoverable state rather than rote copying of blocks ofdata. Examples of replication operations (e.g., continuous datareplication) are provided in U.S. Pat. No. 7,617,262.

Deduplication/Single-Instancing Operations

Deduplication or single-instance storage is useful to reduce the amountof non-primary data. For instance, some or all of the above-describedsecondary copy operations can involve deduplication in some fashion. Newdata is read, broken down into data portions of a selected granularity(e.g., sub-file level blocks, files, etc.), compared with correspondingportions that are already in secondary storage, and only new/changedportions are stored. Portions that already exist are represented aspointers to the already-stored data. Thus, a deduplicated secondary copy116 may comprise actual data portions copied from primary data 112 andmay further comprise pointers to already-stored data, which is generallymore storage-efficient than a full copy.

In order to streamline the comparison process, system 100 may calculateand/or store signatures (e.g., hashes or cryptographically unique IDs)corresponding to the individual source data portions and compare thesignatures to already-stored data signatures, instead of comparingentire data portions. In some cases, only a single instance of each dataportion is stored, and deduplication operations may therefore bereferred to interchangeably as “single-instancing” operations. Dependingon the implementation, however, deduplication operations can store morethan one instance of certain data portions, yet still significantlyreduce stored-data redundancy. Depending on the embodiment,deduplication portions such as data blocks can be of fixed or variablelength. Using variable length blocks can enhance deduplication byresponding to changes in the data stream, but can involve more complexprocessing. In some cases, system 100 utilizes a technique fordynamically aligning deduplication blocks based on changing content inthe data stream, as described in U.S. Pat. No. 8,364,652.

System 100 can deduplicate in a variety of manners at a variety oflocations. For instance, in some embodiments, system 100 implements“target-side” deduplication by deduplicating data at the media agent 144after being received from data agent 142. In some such cases, mediaagents 144 are generally configured to manage the deduplication process.For instance, one or more of the media agents 144 maintain acorresponding deduplication database that stores deduplicationinformation (e.g., datablock signatures). Examples of such aconfiguration are provided in U.S. Pat. No. 9,020,900. Instead of or incombination with “target-side” deduplication, “source-side” (or“client-side”) deduplication can also be performed, e.g., to reduce theamount of data to be transmitted by data agent 142 to media agent 144.Storage manager 140 may communicate with other components within system100 via network protocols and cloud service provider APIs to facilitatecloud-based deduplication/single instancing, as exemplified in U.S. Pat.No. 8,954,446. Some other deduplication/single instancing techniques aredescribed in U.S. Pat. Pub. No. 2006/0224846 and in U.S. Pat. No.9,098,495.

Information Lifecycle Management and Hierarchical Storage Management

In some embodiments, files and other data over their lifetime move frommore expensive quick-access storage to less expensive slower-accessstorage. Operations associated with moving data through various tiers ofstorage are sometimes referred to as information lifecycle management(ILM) operations.

One type of ILM operation is a hierarchical storage management (HSM)operation, which generally automatically moves data between classes ofstorage devices, such as from high-cost to low-cost storage devices. Forinstance, an HSM operation may involve movement of data from primarystorage devices 104 to secondary storage devices 108, or between tiersof secondary storage devices 108. With each tier, the storage devicesmay be progressively cheaper, have relatively slower access/restoretimes, etc. For example, movement of data between tiers may occur asdata becomes less important over time. In some embodiments, an HSMoperation is similar to archiving in that creating an HSM copy may(though not always) involve deleting some of the source data, e.g.,according to one or more criteria related to the source data. Forexample, an HSM copy may include primary data 112 or a secondary copy116 that exceeds a given size threshold or a given age threshold. Often,and unlike some types of archive copies, HSM data that is removed oraged from the source is replaced by a logical reference pointer or stub.The reference pointer or stub can be stored in the primary storagedevice 104 or other source storage device, such as a secondary storagedevice 108 to replace the deleted source data and to point to orotherwise indicate the new location in (another) secondary storagedevice 108.

For example, files are generally moved between higher and lower coststorage depending on how often the files are accessed. When a userrequests access to HSM data that has been removed or migrated, system100 uses the stub to locate the data and may make recovery of the dataappear transparent, even though the HSM data may be stored at a locationdifferent from other source data. In this manner, the data appears tothe user (e.g., in file system browsing windows and the like) as if itstill resides in the source location (e.g., in a primary storage device104). The stub may include metadata associated with the correspondingdata, so that a file system and/or application can provide someinformation about the data object and/or a limited-functionality version(e.g., a preview) of the data object.

An HSM copy may be stored in a format other than the native applicationformat (e.g., compressed, encrypted, deduplicated, and/or otherwisemodified). In some cases, copies which involve the removal of data fromsource storage and the maintenance of stub or other logical referenceinformation on source storage may be referred to generally as “on-linearchive copies.” On the other hand, copies which involve the removal ofdata from source storage without the maintenance of stub or otherlogical reference information on source storage may be referred to as“off-line archive copies.” Examples of HSM and ILM techniques areprovided in U.S. Pat. No. 7,343,453.

Auxiliary Copy Operations

An auxiliary copy is generally a copy of an existing secondary copy 116.For instance, an initial secondary copy 116 may be derived from primarydata 112 or from data residing in secondary storage subsystem 118,whereas an auxiliary copy is generated from the initial secondary copy116. Auxiliary copies provide additional standby copies of data and mayreside on different secondary storage devices 108 than the initialsecondary copies 116. Thus, auxiliary copies can be used for recoverypurposes if initial secondary copies 116 become unavailable. Exemplaryauxiliary copy techniques are described in further detail in U.S. Pat.No. 8,230,195.

Disaster-Recovery Copy Operations

System 100 may also make and retain disaster recovery copies, often assecondary, high-availability disk copies. System 100 may createsecondary copies and store them at disaster recovery locations usingauxiliary copy or replication operations, such as continuous datareplication technologies. Depending on the particular data protectiongoals, disaster recovery locations can be remote from the clientcomputing devices 102 and primary storage devices 104, remote from someor all of the secondary storage devices 108, or both.

Data Manipulation, Including Encryption and Compression

Data manipulation and processing may include encryption and compressionas well as integrity marking and checking, formatting for transmission,formatting for storage, etc. Data may be manipulated “client-side” bydata agent 142 as well as “target-side” by media agent 144 in the courseof creating secondary copy 116, or conversely in the course of restoringdata from secondary to primary.

Encryption Operations

System 100 in some cases is configured to process data (e.g., files orother data objects, primary data 112, secondary copies 116, etc.),according to an appropriate encryption algorithm (e.g., Blowfish,Advanced Encryption Standard (AES), Triple Data Encryption Standard(3-DES), etc.) to limit access and provide data security. System 100 insome cases encrypts the data at the client level, such that clientcomputing devices 102 (e.g., data agents 142) encrypt the data prior totransferring it to other components, e.g., before sending the data tomedia agents 144 during a secondary copy operation. In such cases,client computing device 102 may maintain or have access to an encryptionkey or passphrase for decrypting the data upon restore. Encryption canalso occur when media agent 144 creates auxiliary copies or archivecopies. Encryption may be applied in creating a secondary copy 116 of apreviously unencrypted secondary copy 116, without limitation. Infurther embodiments, secondary storage devices 108 can implementbuilt-in, high performance hardware-based encryption.

Compression Operations

Similar to encryption, system 100 may also or alternatively compressdata in the course of generating a secondary copy 116. Compressionencodes information such that fewer bits are needed to represent theinformation as compared to the original representation. Compressiontechniques are well known in the art. Compression operations may applyone or more data compression algorithms. Compression may be applied increating a secondary copy 116 of a previously uncompressed secondarycopy, e.g., when making archive copies or disaster recovery copies. Theuse of compression may result in metadata that specifies the nature ofthe compression, so that data may be uncompressed on restore ifappropriate.

Data Analysis, Reporting, and Management Operations

Data analysis, reporting, and management operations can differ from datamovement operations in that they do not necessarily involve copying,migration or other transfer of data between different locations in thesystem. For instance, data analysis operations may involve processing(e.g., offline processing) or modification of already stored primarydata 112 and/or secondary copies 116. However, in some embodiments dataanalysis operations are performed in conjunction with data movementoperations. Some data analysis operations include content indexingoperations and classification operations which can be useful inleveraging data under management to enhance search and other features.

Classification Operations/Content Indexing

In some embodiments, information management system 100 analyzes andindexes characteristics, content, and metadata associated with primarydata 112 (“online content indexing”) and/or secondary copies 116(“off-line content indexing”). Content indexing can identify files orother data objects based on content (e.g., user-defined keywords orphrases, other keywords/phrases that are not defined by a user, etc.),and/or metadata (e.g., email metadata such as “to,” “from,” “cc,” “bcc,”attachment name, received time, etc.). Content indexes may be searchedand search results may be restored.

System 100 generally organizes and catalogues the results into a contentindex, which may be stored within media agent database 152, for example.The content index can also include the storage locations of or pointerreferences to indexed data in primary data 112 and/or secondary copies116. Results may also be stored elsewhere in system 100 (e.g., inprimary storage device 104 or in secondary storage device 108). Suchcontent index data provides storage manager 140 or other components withan efficient mechanism for locating primary data 112 and/or secondarycopies 116 of data objects that match particular criteria, thus greatlyincreasing the search speed capability of system 100. For instance,search criteria can be specified by a user through user interface 158 ofstorage manager 140. Moreover, when system 100 analyzes data and/ormetadata in secondary copies 116 to create an “off-line content index,”this operation has no significant impact on the performance of clientcomputing devices 102 and thus does not take a toll on the productionenvironment. Examples of content indexing techniques are provided inU.S. Pat. No. 8,170,995.

One or more components, such as a content index engine, can beconfigured to scan data and/or associated metadata for classificationpurposes to populate a database (or other data structure) ofinformation, which can be referred to as a “data classificationdatabase” or a “metabase.” Depending on the embodiment, the dataclassification database(s) can be organized in a variety of differentways, including centralization, logical sub-divisions, and/or physicalsub-divisions. For instance, one or more data classification databasesmay be associated with different subsystems or tiers within system 100.As an example, there may be a first metabase associated with primarystorage subsystem 117 and a second metabase associated with secondarystorage subsystem 118. In other cases, metabase(s) may be associatedwith individual components, e.g., client computing devices 102 and/ormedia agents 144. In some embodiments, a data classification databasemay reside as one or more data structures within management database146, may be otherwise associated with storage manager 140, and/or mayreside as a separate component. In some cases, metabase(s) may beincluded in separate database(s) and/or on separate storage device(s)from primary data 112 and/or secondary copies 116, such that operationsrelated to the metabase(s) do not significantly impact performance onother components of system 100. In other cases, metabase(s) may bestored along with primary data 112 and/or secondary copies 116. Files orother data objects can be associated with identifiers (e.g., tagentries, etc.) to facilitate searches of stored data objects. Among anumber of other benefits, the metabase can also allow efficient,automatic identification of files or other data objects to associatewith secondary copy or other information management operations. Forinstance, a metabase can dramatically improve the speed with whichsystem 100 can search through and identify data as compared to otherapproaches that involve scanning an entire file system. Examples ofmetabases and data classification operations are provided in U.S. Pat.Nos. 7,734,669 and 7,747,579.

Management and Reporting Operations

Certain embodiments leverage the integrated ubiquitous nature of system100 to provide useful system-wide management and reporting. Operationsmanagement can generally include monitoring and managing the health andperformance of system 100 by, without limitation, performing errortracking, generating granular storage/performance metrics (e.g., jobsuccess/failure information, deduplication efficiency, etc.), generatingstorage modeling and costing information, and the like. As an example,storage manager 140 or another component in system 100 may analyzetraffic patterns and suggest and/or automatically route data to minimizecongestion. In some embodiments, the system can generate predictionsrelating to storage operations or storage operation information. Suchpredictions, which may be based on a trending analysis, may predictvarious network operations or resource usage, such as network trafficlevels, storage media use, use of bandwidth of communication links, useof media agent components, etc. Further examples of traffic analysis,trend analysis, prediction generation, and the like are described inU.S. Pat. No. 7,343,453.

In some configurations having a hierarchy of storage operation cells, amaster storage manager 140 may track the status of subordinate cells,such as the status of jobs, system components, system resources, andother items, by communicating with storage managers 140 (or othercomponents) in the respective storage operation cells. Moreover, themaster storage manager 140 may also track status by receiving periodicstatus updates from the storage managers 140 (or other components) inthe respective cells regarding jobs, system components, systemresources, and other items. In some embodiments, a master storagemanager 140 may store status information and other information regardingits associated storage operation cells and other system information inits management database 146 and/or index 150 (or in another location).The master storage manager 140 or other component may also determinewhether certain storage-related or other criteria are satisfied, and mayperform an action or trigger event (e.g., data migration) in response tothe criteria being satisfied, such as where a storage threshold is metfor a particular volume, or where inadequate protection exists forcertain data. For instance, data from one or more storage operationcells is used to dynamically and automatically mitigate recognizedrisks, and/or to advise users of risks or suggest actions to mitigatethese risks. For example, an information management policy may specifycertain requirements (e.g., that a storage device should maintain acertain amount of free space, that secondary copies should occur at aparticular interval, that data should be aged and migrated to otherstorage after a particular period, that data on a secondary volumeshould always have a certain level of availability and be restorablewithin a given time period, that data on a secondary volume may bemirrored or otherwise migrated to a specified number of other volumes,etc.). If a risk condition or other criterion is triggered, the systemmay notify the user of these conditions and may suggest (orautomatically implement) a mitigation action to address the risk. Forexample, the system may indicate that data from a primary copy 112should be migrated to a secondary storage device 108 to free up space onprimary storage device 104. Examples of the use of risk factors andother triggering criteria are described in U.S. Pat. No. 7,343,453.

In some embodiments, system 100 may also determine whether a metric orother indication satisfies particular storage criteria sufficient toperform an action. For example, a storage policy or other definitionmight indicate that a storage manager 140 should initiate a particularaction if a storage metric or other indication drops below or otherwisefails to satisfy specified criteria such as a threshold of dataprotection. In some embodiments, risk factors may be quantified intocertain measurable service or risk levels. For example, certainapplications and associated data may be considered to be more importantrelative to other data and services. Financial compliance data, forexample, may be of greater importance than marketing materials, etc.Network administrators may assign priority values or “weights” tocertain data and/or applications corresponding to the relativeimportance. The level of compliance of secondary copy operationsspecified for these applications may also be assigned a certain value.Thus, the health, impact, and overall importance of a service may bedetermined, such as by measuring the compliance value and calculatingthe product of the priority value and the compliance value to determinethe “service level” and comparing it to certain operational thresholdsto determine whether it is acceptable. Further examples of the servicelevel determination are provided in U.S. Pat. No. 7,343,453.

System 100 may additionally calculate data costing and data availabilityassociated with information management operation cells. For instance,data received from a cell may be used in conjunction withhardware-related information and other information about system elementsto determine the cost of storage and/or the availability of particulardata. Exemplary information generated could include how fast aparticular department is using up available storage space, how long datawould take to recover over a particular pathway from a particularsecondary storage device, costs over time, etc. Moreover, in someembodiments, such information may be used to determine or predict theoverall cost associated with the storage of certain information. Thecost associated with hosting a certain application may be based, atleast in part, on the type of media on which the data resides, forexample. Storage devices may be assigned to a particular costcategories, for example. Further examples of costing techniques aredescribed in U.S. Pat. No. 7,343,453.

Any of the above types of information (e.g., information related totrending, predictions, job, cell or component status, risk, servicelevel, costing, etc.) can generally be provided to users via userinterface 158 in a single integrated view or console (not shown). Reporttypes may include: scheduling, event management, media management anddata aging. Available reports may also include backup history, dataaging history, auxiliary copy history, job history, library and drive,media in library, restore history, and storage policy, etc., withoutlimitation. Such reports may be specified and created at a certain pointin time as a system analysis, forecasting, or provisioning tool.Integrated reports may also be generated that illustrate storage andperformance metrics, risks and storage costing information. Moreover,users may create their own reports based on specific needs. Userinterface 158 can include an option to graphically depict the variouscomponents in the system using appropriate icons. As one example, userinterface 158 may provide a graphical depiction of primary storagedevices 104, secondary storage devices 108, data agents 142 and/or mediaagents 144, and their relationship to one another in system 100.

In general, the operations management functionality of system 100 canfacilitate planning and decision-making. For example, in someembodiments, a user may view the status of some or all jobs as well asthe status of each component of information management system 100. Usersmay then plan and make decisions based on this data. For instance, auser may view high-level information regarding secondary copy operationsfor system 100, such as job status, component status, resource status(e.g., communication pathways, etc.), and other information. The usermay also drill down or use other means to obtain more detailedinformation regarding a particular component, job, or the like. Furtherexamples are provided in U.S. Pat. No. 7,343,453.

System 100 can also be configured to perform system-wide e-discoveryoperations in some embodiments. In general, e-discovery operationsprovide a unified collection and search capability for data in thesystem, such as data stored in secondary storage devices 108 (e.g.,backups, archives, or other secondary copies 116). For example, system100 may construct and maintain a virtual repository for data stored insystem 100 that is integrated across source applications 110, differentstorage device types, etc. According to some embodiments, e-discoveryutilizes other techniques described herein, such as data classificationand/or content indexing.

Information Management Policies

An information management policy 148 can include a data structure orother information source that specifies a set of parameters (e.g.,criteria and rules) associated with secondary copy and/or otherinformation management operations.

One type of information management policy 148 is a “storage policy.”According to certain embodiments, a storage policy generally comprises adata structure or other information source that defines (or includesinformation sufficient to determine) a set of preferences or othercriteria for performing information management operations. Storagepolicies can include one or more of the following: (1) what data will beassociated with the storage policy, e.g., subclient; (2) a destinationto which the data will be stored; (3) datapath information specifyinghow the data will be communicated to the destination; (4) the type ofsecondary copy operation to be performed; and (5) retention informationspecifying how long the data will be retained at the destination (see,e.g., FIG. 1E). Data associated with a storage policy can be logicallyorganized into subclients, which may represent primary data 112 and/orsecondary copies 116. A subclient may represent static or dynamicassociations of portions of a data volume. Subclients may representmutually exclusive portions. Thus, in certain embodiments, a portion ofdata may be given a label and the association is stored as a staticentity in an index, database or other storage location. Subclients mayalso be used as an effective administrative scheme of organizing dataaccording to data type, department within the enterprise, storagepreferences, or the like. Depending on the configuration, subclients cancorrespond to files, folders, virtual machines, databases, etc. In oneexemplary scenario, an administrator may find it preferable to separatee-mail data from financial data using two different subclients.

A storage policy can define where data is stored by specifying a targetor destination storage device (or group of storage devices). Forinstance, where the secondary storage device 108 includes a group ofdisk libraries, the storage policy may specify a particular disk libraryfor storing the subclients associated with the policy. As anotherexample, where the secondary storage devices 108 include one or moretape libraries, the storage policy may specify a particular tape libraryfor storing the subclients associated with the storage policy, and mayalso specify a drive pool and a tape pool defining a group of tapedrives and a group of tapes, respectively, for use in storing thesubclient data. While information in the storage policy can bestatically assigned in some cases, some or all of the information in thestorage policy can also be dynamically determined based on criteria setforth in the storage policy. For instance, based on such criteria, aparticular destination storage device(s) or other parameter of thestorage policy may be determined based on characteristics associatedwith the data involved in a particular secondary copy operation, deviceavailability (e.g., availability of a secondary storage device 108 or amedia agent 144), network status and conditions (e.g., identifiedbottlenecks), user credentials, and the like.

Datapath information can also be included in the storage policy. Forinstance, the storage policy may specify network pathways and componentsto utilize when moving the data to the destination storage device(s). Insome embodiments, the storage policy specifies one or more media agents144 for conveying data associated with the storage policy between thesource and destination. A storage policy can also specify the type(s) ofassociated operations, such as backup, archive, snapshot, auxiliarycopy, or the like. Furthermore, retention parameters can specify howlong the resulting secondary copies 116 will be kept (e.g., a number ofdays, months, years, etc.), perhaps depending on organizational needsand/or compliance criteria.

When adding a new client computing device 102, administrators canmanually configure information management policies 148 and/or othersettings, e.g., via user interface 158. However, this can be an involvedprocess resulting in delays, and it may be desirable to begin dataprotection operations quickly, without awaiting human intervention.Thus, in some embodiments, system 100 automatically applies a defaultconfiguration to client computing device 102. As one example, when oneor more data agent(s) 142 are installed on a client computing device102, the installation script may register the client computing device102 with storage manager 140, which in turn applies the defaultconfiguration to the new client computing device 102. In this manner,data protection operations can begin substantially immediately. Thedefault configuration can include a default storage policy, for example,and can specify any appropriate information sufficient to begin dataprotection operations. This can include a type of data protectionoperation, scheduling information, a target secondary storage device108, data path information (e.g., a particular media agent 144), and thelike.

Another type of information management policy 148 is a “schedulingpolicy,” which specifies when and how often to perform operations.Scheduling parameters may specify with what frequency (e.g., hourly,weekly, daily, event-based, etc.) or under what triggering conditionssecondary copy or other information management operations are to takeplace. Scheduling policies in some cases are associated with particularcomponents, such as a subclient, client computing device 102, and thelike.

Another type of information management policy 148 is an “audit policy”(or “security policy”), which comprises preferences, rules and/orcriteria that protect sensitive data in system 100. For example, anaudit policy may define “sensitive objects” which are files or dataobjects that contain particular keywords (e.g., “confidential,” or“privileged”) and/or are associated with particular keywords (e.g., inmetadata) or particular flags (e.g., in metadata identifying a documentor email as personal, confidential, etc.). An audit policy may furtherspecify rules for handling sensitive objects. As an example, an auditpolicy may require that a reviewer approve the transfer of any sensitiveobjects to a cloud storage site, and that if approval is denied for aparticular sensitive object, the sensitive object should be transferredto a local primary storage device 104 instead. To facilitate thisapproval, the audit policy may further specify how a secondary storagecomputing device 106 or other system component should notify a reviewerthat a sensitive object is slated for transfer.

Another type of information management policy 148 is a “provisioningpolicy,” which can include preferences, priorities, rules, and/orcriteria that specify how client computing devices 102 (or groupsthereof) may utilize system resources, such as available storage oncloud storage and/or network bandwidth. A provisioning policy specifies,for example, data quotas for particular client computing devices 102(e.g., a number of gigabytes that can be stored monthly, quarterly orannually). Storage manager 140 or other components may enforce theprovisioning policy. For instance, media agents 144 may enforce thepolicy when transferring data to secondary storage devices 108. If aclient computing device 102 exceeds a quota, a budget for the clientcomputing device 102 (or associated department) may be adjustedaccordingly or an alert may trigger.

While the above types of information management policies 148 aredescribed as separate policies, one or more of these can be generallycombined into a single information management policy 148. For instance,a storage policy may also include or otherwise be associated with one ormore scheduling, audit, or provisioning policies or operationalparameters thereof. Moreover, while storage policies are typicallyassociated with moving and storing data, other policies may beassociated with other types of information management operations. Thefollowing is a non-exhaustive list of items that information managementpolicies 148 may specify:

-   -   schedules or other timing information, e.g., specifying when        and/or how often to perform information management operations;    -   the type of secondary copy 116 and/or copy format (e.g.,        snapshot, backup, archive, HSM, etc.);    -   a location or a class or quality of storage for storing        secondary copies 116 (e.g., one or more particular secondary        storage devices 108);    -   preferences regarding whether and how to encrypt, compress,        deduplicate, or otherwise modify or transform secondary copies        116;    -   which system components and/or network pathways (e.g., preferred        media agents 144) should be used to perform secondary storage        operations;    -   resource allocation among different computing devices or other        system components used in performing information management        operations (e.g., bandwidth allocation, available storage        capacity, etc.);    -   whether and how to synchronize or otherwise distribute files or        other data objects across multiple computing devices or hosted        services; and    -   retention information specifying the length of time primary data        112 and/or secondary copies 116 should be retained, e.g., in a        particular class or tier of storage devices, or within the        system 100.

Information management policies 148 can additionally specify or dependon historical or current criteria that may be used to determine whichrules to apply to a particular data object, system component, orinformation management operation, such as:

-   -   frequency with which primary data 112 or a secondary copy 116 of        a data object or metadata has been or is predicted to be used,        accessed, or modified;    -   time-related factors (e.g., aging information such as time since        the creation or modification of a data object);    -   deduplication information (e.g., hashes, data blocks,        deduplication block size, deduplication efficiency or other        metrics);    -   an estimated or historic usage or cost associated with different        components (e.g., with secondary storage devices 108);    -   the identity of users, applications 110, client computing        devices 102 and/or other computing devices that created,        accessed, modified, or otherwise utilized primary data 112 or        secondary copies 116;    -   a relative sensitivity (e.g., confidentiality, importance) of a        data object, e.g., as determined by its content and/or metadata;    -   the current or historical storage capacity of various storage        devices;    -   the current or historical network capacity of network pathways        connecting various components within the storage operation cell;    -   access control lists or other security information; and    -   the content of a particular data object (e.g., its textual        content) or of metadata associated with the data object.

Exemplary Storage Policy and Secondary Copy Operations

FIG. 1E includes a data flow diagram depicting performance of secondarycopy operations by an embodiment of information management system 100,according to an exemplary storage policy 148A. System 100 includes astorage manager 140, a client computing device 102 having a file systemdata agent 142A and an email data agent 142B operating thereon, aprimary storage device 104, two media agents 144A, 144B, and twosecondary storage devices 108: a disk library 108A and a tape library108B. As shown, primary storage device 104 includes primary data 112A,which is associated with a logical grouping of data associated with afile system (“file system subclient”), and primary data 112B, which is alogical grouping of data associated with email (“email subclient”). Thetechniques described with respect to FIG. 1E can be utilized inconjunction with data that is otherwise organized as well.

As indicated by the dashed box, the second media agent 144B and tapelibrary 1088 are “off-site,” and may be remotely located from the othercomponents in system 100 (e.g., in a different city, office building,etc.). Indeed, “off-site” may refer to a magnetic tape located in remotestorage, which must be manually retrieved and loaded into a tape driveto be read. In this manner, information stored on the tape library 108Bmay provide protection in the event of a disaster or other failure atthe main site(s) where data is stored.

The file system subclient 112A in certain embodiments generallycomprises information generated by the file system and/or operatingsystem of client computing device 102, and can include, for example,file system data (e.g., regular files, file tables, mount points, etc.),operating system data (e.g., registries, event logs, etc.), and thelike. The e-mail subclient 1128 can include data generated by an e-mailapplication operating on client computing device 102, e.g., mailboxinformation, folder information, emails, attachments, associateddatabase information, and the like. As described above, the subclientscan be logical containers, and the data included in the correspondingprimary data 112A and 112B may or may not be stored contiguously.

The exemplary storage policy 148A includes backup copy preferences orrule set 160, disaster recovery copy preferences or rule set 162, andcompliance copy preferences or rule set 164. Backup copy rule set 160specifies that it is associated with file system subclient 166 and emailsubclient 168. Each of subclients 166 and 168 are associated with theparticular client computing device 102. Backup copy rule set 160 furtherspecifies that the backup operation will be written to disk library 108Aand designates a particular media agent 144A to convey the data to disklibrary 108A. Finally, backup copy rule set 160 specifies that backupcopies created according to rule set 160 are scheduled to be generatedhourly and are to be retained for 30 days. In some other embodiments,scheduling information is not included in storage policy 148A and isinstead specified by a separate scheduling policy.

Disaster recovery copy rule set 162 is associated with the same twosubclients 166 and 168. However, disaster recovery copy rule set 162 isassociated with tape library 108B, unlike backup copy rule set 160.Moreover, disaster recovery copy rule set 162 specifies that a differentmedia agent, namely 144B, will convey data to tape library 108B.Disaster recovery copies created according to rule set 162 will beretained for 60 days and will be generated daily. Disaster recoverycopies generated according to disaster recovery copy rule set 162 canprovide protection in the event of a disaster or other catastrophic dataloss that would affect the backup copy 116A maintained on disk library108A.

Compliance copy rule set 164 is only associated with the email subclient168, and not the file system subclient 166. Compliance copies generatedaccording to compliance copy rule set 164 will therefore not includeprimary data 112A from the file system subclient 166. For instance, theorganization may be under an obligation to store and maintain copies ofemail data for a particular period of time (e.g., 10 years) to complywith state or federal regulations, while similar regulations do notapply to file system data. Compliance copy rule set 164 is associatedwith the same tape library 108B and media agent 144B as disasterrecovery copy rule set 162, although a different storage device or mediaagent could be used in other embodiments. Finally, compliance copy ruleset 164 specifies that the copies it governs will be generated quarterlyand retained for 10 years.

Secondary Copy Jobs

A logical grouping of secondary copy operations governed by a rule setand being initiated at a point in time may be referred to as a“secondary copy job” (and sometimes may be called a “backup job,” eventhough it is not necessarily limited to creating only backup copies).Secondary copy jobs may be initiated on demand as well. Steps 1-9 belowillustrate three secondary copy jobs based on storage policy 148A.

Referring to FIG. 1E, at step 1, storage manager 140 initiates a backupjob according to the backup copy rule set 160, which logically comprisesall the secondary copy operations necessary to effectuate rules 160 instorage policy 148A every hour, including steps 1-4 occurring hourly.For instance, a scheduling service running on storage manager 140accesses backup copy rule set 160 or a separate scheduling policyassociated with client computing device 102 and initiates a backup jobon an hourly basis. Thus, at the scheduled time, storage manager 140sends instructions to client computing device 102 (i.e., to both dataagent 142A and data agent 142B) to begin the backup job.

At step 2, file system data agent 142A and email data agent 142B onclient computing device 102 respond to instructions from storage manager140 by accessing and processing the respective subclient primary data112A and 112B involved in the backup copy operation, which can be foundin primary storage device 104. Because the secondary copy operation is abackup copy operation, the data agent(s) 142A, 142B may format the datainto a backup format or otherwise process the data suitable for a backupcopy.

At step 3, client computing device 102 communicates the processed filesystem data (e.g., using file system data agent 142A) and the processedemail data (e.g., using email data agent 142B) to the first media agent144A according to backup copy rule set 160, as directed by storagemanager 140. Storage manager 140 may further keep a record in managementdatabase 146 of the association between media agent 144A and one or moreof: client computing device 102, file system subclient 112A, file systemdata agent 142A, email subclient 112B, email data agent 142B, and/orbackup copy 116A.

The target media agent 144A receives the data-agent-processed data fromclient computing device 102, and at step 4 generates and conveys backupcopy 116A to disk library 108A to be stored as backup copy 116A, againat the direction of storage manager 140 and according to backup copyrule set 160. Media agent 144A can also update its index 153 to includedata and/or metadata related to backup copy 116A, such as informationindicating where the backup copy 116A resides on disk library 108A,where the email copy resides, where the file system copy resides, dataand metadata for cache retrieval, etc. Storage manager 140 may similarlyupdate its index 150 to include information relating to the secondarycopy operation, such as information relating to the type of operation, aphysical location associated with one or more copies created by theoperation, the time the operation was performed, status informationrelating to the operation, the components involved in the operation, andthe like. In some cases, storage manager 140 may update its index 150 toinclude some or all of the information stored in index 153 of mediaagent 144A. At this point, the backup job may be considered complete.After the 30-day retention period expires, storage manager 140 instructsmedia agent 144A to delete backup copy 116A from disk library 108A andindexes 150 and/or 153 are updated accordingly.

At step 5, storage manager 140 initiates another backup job for adisaster recovery copy according to the disaster recovery rule set 162.Illustratively this includes steps 5-7 occurring daily for creatingdisaster recovery copy 1168. Illustratively, and by way of illustratingthe scalable aspects and off-loading principles embedded in system 100,disaster recovery copy 1168 is based on backup copy 116A and not onprimary data 112A and 112B.

At step 6, illustratively based on instructions received from storagemanager 140 at step 5, the specified media agent 1448 retrieves the mostrecent backup copy 116A from disk library 108A.

At step 7, again at the direction of storage manager 140 and asspecified in disaster recovery copy rule set 162, media agent 144B usesthe retrieved data to create a disaster recovery copy 1168 and store itto tape library 1088. In some cases, disaster recovery copy 1168 is adirect, mirror copy of backup copy 116A, and remains in the backupformat. In other embodiments, disaster recovery copy 1168 may be furthercompressed or encrypted, or may be generated in some other manner, suchas by using primary data 112A and 1128 from primary storage device 104as sources. The disaster recovery copy operation is initiated once a dayand disaster recovery copies 1168 are deleted after 60 days; indexes 153and/or 150 are updated accordingly when/after each informationmanagement operation is executed and/or completed. The present backupjob may be considered completed.

At step 8, storage manager 140 initiates another backup job according tocompliance rule set 164, which performs steps 8-9 quarterly to createcompliance copy 116C. For instance, storage manager 140 instructs mediaagent 1448 to create compliance copy 116C on tape library 1088, asspecified in the compliance copy rule set 164.

At step 9 in the example, compliance copy 116C is generated usingdisaster recovery copy 1168 as the source. This is efficient, becausedisaster recovery copy resides on the same secondary storage device andthus no network resources are required to move the data. In otherembodiments, compliance copy 116C is instead generated using primarydata 1128 corresponding to the email subclient or using backup copy 116Afrom disk library 108A as source data. As specified in the illustratedexample, compliance copies 116C are created quarterly, and are deletedafter ten years, and indexes 153 and/or 150 are kept up-to-dateaccordingly.

Exemplary Applications of Storage Policies—Information GovernancePolicies and Classification

Again referring to FIG. 1E, storage manager 140 may permit a user tospecify aspects of storage policy 148A. For example, the storage policycan be modified to include information governance policies to define howdata should be managed in order to comply with a certain regulation orbusiness objective. The various policies may be stored, for example, inmanagement database 146. An information governance policy may align withone or more compliance tasks that are imposed by regulations or businessrequirements. Examples of information governance policies might includea Sarbanes-Oxley policy, a HIPAA policy, an electronic discovery(e-discovery) policy, and so on.

Information governance policies allow administrators to obtain differentperspectives on an organization's online and offline data, without theneed for a dedicated data silo created solely for each differentviewpoint. As described previously, the data storage systems hereinbuild an index that reflects the contents of a distributed data set thatspans numerous clients and storage devices, including both primary dataand secondary copies, and online and offline copies. An organization mayapply multiple information governance policies in a top-down manner overthat unified data set and indexing schema in order to view andmanipulate the data set through different lenses, each of which isadapted to a particular compliance or business goal. Thus, for example,by applying an e-discovery policy and a Sarbanes-Oxley policy, twodifferent groups of users in an organization can conduct two verydifferent analyses of the same underlying physical set of data/copies,which may be distributed throughout the information management system.

An information governance policy may comprise a classification policy,which defines a taxonomy of classification terms or tags relevant to acompliance task and/or business objective. A classification policy mayalso associate a defined tag with a classification rule. Aclassification rule defines a particular combination of criteria, suchas users who have created, accessed or modified a document or dataobject; file or application types; content or metadata keywords; clientsor storage locations; dates of data creation and/or access; reviewstatus or other status within a workflow (e.g., reviewed orun-reviewed); modification times or types of modifications; and/or anyother data attributes in any combination, without limitation. Aclassification rule may also be defined using other classification tagsin the taxonomy. The various criteria used to define a classificationrule may be combined in any suitable fashion, for example, via Booleanoperators, to define a complex classification rule. As an example, ane-discovery classification policy might define a classification tag“privileged” that is associated with documents or data objects that (1)were created or modified by legal department staff, or (2) were sent toor received from outside counsel via email, or (3) contain one of thefollowing keywords: “privileged” or “attorney” or “counsel,” or otherlike terms. Accordingly, all these documents or data objects will beclassified as “privileged.”

One specific type of classification tag, which may be added to an indexat the time of indexing, is an “entity tag.” An entity tag may be, forexample, any content that matches a defined data mask format. Examplesof entity tags might include, e.g., social security numbers (e.g., anynumerical content matching the formatting mask XXX-XX-XXXX), credit cardnumbers (e.g., content having a 13-16 digit string of numbers), SKUnumbers, product numbers, etc. A user may define a classification policyby indicating criteria, parameters or descriptors of the policy via agraphical user interface, such as a form or page with fields to befilled in, pull-down menus or entries allowing one or more of severaloptions to be selected, buttons, sliders, hypertext links or other knownuser interface tools for receiving user input, etc. For example, a usermay define certain entity tags, such as a particular product number orproject ID. In some implementations, the classification policy can beimplemented using cloud-based techniques. For example, the storagedevices may be cloud storage devices, and the storage manager 140 mayexecute cloud service provider API over a network to classify datastored on cloud storage devices.

Restore Operations from Secondary Copies

While not shown in FIG. 1E, at some later point in time, a restoreoperation can be initiated involving one or more of secondary copies116A, 116B, and 116C. A restore operation logically takes a selectedsecondary copy 116, reverses the effects of the secondary copy operationthat created it, and stores the restored data to primary storage where aclient computing device 102 may properly access it as primary data. Amedia agent 144 and an appropriate data agent 142 (e.g., executing onthe client computing device 102) perform the tasks needed to complete arestore operation. For example, data that was encrypted, compressed,and/or deduplicated in the creation of secondary copy 116 will becorrespondingly rehydrated (reversing deduplication), uncompressed, andunencrypted into a format appropriate to primary data. Metadata storedwithin or associated with the secondary copy 116 may be used during therestore operation. In general, restored data should be indistinguishablefrom other primary data 112. Preferably, the restored data has fullyregained the native format that may make it immediately usable byapplication 110.

As one example, a user may manually initiate a restore of backup copy116A, e.g., by interacting with user interface 158 of storage manager140 or with a web-based console with access to system 100. Storagemanager 140 may accesses data in its index 150 and/or managementdatabase 146 (and/or the respective storage policy 148A) associated withthe selected backup copy 116A to identify the appropriate media agent144A and/or secondary storage device 108A where the secondary copyresides. The user may be presented with a representation (e.g., stub,thumbnail, listing, etc.) and metadata about the selected secondarycopy, in order to determine whether this is the appropriate copy to berestored, e.g., date that the original primary data was created. Storagemanager 140 will then instruct media agent 144A and an appropriate dataagent 142 on the target client computing device 102 to restore secondarycopy 116A to primary storage device 104. A media agent may be selectedfor use in the restore operation based on a load balancing algorithm, anavailability based algorithm, or other criteria. The selected mediaagent, e.g., 144A, retrieves secondary copy 116A from disk library 108A.For instance, media agent 144A may access its index 153 to identify alocation of backup copy 116A on disk library 108A, or may accesslocation information residing on disk library 108A itself.

In some cases a backup copy 116A that was recently created or accessed,may be cached to speed up the restore operation. In such a case, mediaagent 144A accesses a cached version of backup copy 116A residing inindex 153, without having to access disk library 108A for some or all ofthe data. Once it has retrieved backup copy 116A, the media agent 144Acommunicates the data to the requesting client computing device 102.Upon receipt, file system data agent 142A and email data agent 142B mayunpack (e.g., restore from a backup format to the native applicationformat) the data in backup copy 116A and restore the unpackaged data toprimary storage device 104. In general, secondary copies 116 may berestored to the same volume or folder in primary storage device 104 fromwhich the secondary copy was derived; to another storage location orclient computing device 102; to shared storage, etc. In some cases, thedata may be restored so that it may be used by an application 110 of adifferent version/vintage from the application that created the originalprimary data 112.

Exemplary Secondary Copy Formatting

The formatting and structure of secondary copies 116 can vary dependingon the embodiment. In some cases, secondary copies 116 are formatted asa series of logical data units or “chunks” (e.g., 512 MB, 1 GB, 2 GB, 4GB, or 8 GB chunks). This can facilitate efficient communication andwriting to secondary storage devices 108, e.g., according to resourceavailability. For example, a single secondary copy 116 may be written ona chunk-by-chunk basis to one or more secondary storage devices 108. Insome cases, users can select different chunk sizes, e.g., to improvethroughput to tape storage devices. Generally, each chunk can include aheader and a payload. The payload can include files (or other dataunits) or subsets thereof included in the chunk, whereas the chunkheader generally includes metadata relating to the chunk, some or all ofwhich may be derived from the payload. For example, during a secondarycopy operation, media agent 144, storage manager 140, or other componentmay divide files into chunks and generate headers for each chunk byprocessing the files. Headers can include a variety of information suchas file and/or volume identifier(s), offset(s), and/or other informationassociated with the payload data items, a chunk sequence number, etc.Importantly, in addition to being stored with secondary copy 116 onsecondary storage device 108, chunk headers can also be stored to index153 of the associated media agent(s) 144 and/or to index 150 associatedwith storage manager 140. This can be useful for providing fasterprocessing of secondary copies 116 during browsing, restores, or otheroperations. In some cases, once a chunk is successfully transferred to asecondary storage device 108, the secondary storage device 108 returnsan indication of receipt, e.g., to media agent 144 and/or storagemanager 140, which may update their respective indexes 153, 150accordingly. During restore, chunks may be processed (e.g., by mediaagent 144) according to the information in the chunk header toreassemble the files.

Data can also be communicated within system 100 in data channels thatconnect client computing devices 102 to secondary storage devices 108.These data channels can be referred to as “data streams,” and multipledata streams can be employed to parallelize an information managementoperation, improving data transfer rate, among other advantages. Exampledata formatting techniques including techniques involving datastreaming, chunking, and the use of other data structures in creatingsecondary copies are described in U.S. Pat. Nos. 7,315,923, 8,156,086,and 8,578,120.

FIGS. 1F and 1G are diagrams of example data streams 170 and 171,respectively, which may be employed for performing informationmanagement operations. Referring to FIG. 1F, data agent 142 forms datastream 170 from source data associated with a client computing device102 (e.g., primary data 112). Data stream 170 is composed of multiplepairs of stream header 172 and stream data (or stream payload) 174. Datastreams 170 and 171 shown in the illustrated example are for asingle-instanced storage operation, and a stream payload 174 thereforemay include both single-instance (SI) data and/or non-SI data. A streamheader 172 includes metadata about the stream payload 174. This metadatamay include, for example, a length of the stream payload 174, anindication of whether the stream payload 174 is encrypted, an indicationof whether the stream payload 174 is compressed, an archive fileidentifier (ID), an indication of whether the stream payload 174 issingle instanceable, and an indication of whether the stream payload 174is a start of a block of data.

Referring to FIG. 1G, data stream 171 has the stream header 172 andstream payload 174 aligned into multiple data blocks. In this example,the data blocks are of size 64 KB. The first two stream header 172 andstream payload 174 pairs comprise a first data block of size 64 KB. Thefirst stream header 172 indicates that the length of the succeedingstream payload 174 is 63 KB and that it is the start of a data block.The next stream header 172 indicates that the succeeding stream payload174 has a length of 1 KB and that it is not the start of a new datablock. Immediately following stream payload 174 is a pair comprising anidentifier header 176 and identifier data 178. The identifier header 176includes an indication that the succeeding identifier data 178 includesthe identifier for the immediately previous data block. The identifierdata 178 includes the identifier that the data agent 142 generated forthe data block. The data stream 171 also includes other stream header172 and stream payload 174 pairs, which may be for SI data and/or non-SIdata.

FIG. 1H is a diagram illustrating data structures 180 that may be usedto store blocks of SI data and non-SI data on a storage device (e.g.,secondary storage device 108). According to certain embodiments, datastructures 180 do not form part of a native file system of the storagedevice. Data structures 180 include one or more volume folders 182, oneor more chunk folders 184/185 within the volume folder 182, and multiplefiles within chunk folder 184. Each chunk folder 184/185 includes ametadata file 186/187, a metadata index file 188/189, one or morecontainer files 190/191/193, and a container index file 192/194.Metadata file 186/187 stores non-SI data blocks as well as links to SIdata blocks stored in container files. Metadata index file 188/189stores an index to the data in the metadata file 186/187. Containerfiles 190/191/193 store SI data blocks. Container index file 192/194stores an index to container files 190/191/193. Among other things,container index file 192/194 stores an indication of whether acorresponding block in a container file 190/191/193 is referred to by alink in a metadata file 186/187. For example, data block B2 in thecontainer file 190 is referred to by a link in metadata file 187 inchunk folder 185. Accordingly, the corresponding index entry incontainer index file 192 indicates that data block B2 in container file190 is referred to. As another example, data block B1 in container file191 is referred to by a link in metadata file 187, and so thecorresponding index entry in container index file 192 indicates thatthis data block is referred to.

As an example, data structures 180 illustrated in FIG. 1H may have beencreated as a result of separate secondary copy operations involving twoclient computing devices 102. For example, a first secondary copyoperation on a first client computing device 102 could result in thecreation of the first chunk folder 184, and a second secondary copyoperation on a second client computing device 102 could result in thecreation of the second chunk folder 185. Container files 190/191 in thefirst chunk folder 184 would contain the blocks of SI data of the firstclient computing device 102. If the two client computing devices 102have substantially similar data, the second secondary copy operation onthe data of the second client computing device 102 would result in mediaagent 144 storing primarily links to the data blocks of the first clientcomputing device 102 that are already stored in the container files190/191. Accordingly, while a first secondary copy operation may resultin storing nearly all of the data subject to the operation, subsequentsecondary storage operations involving similar data may result insubstantial data storage space savings, because links to already storeddata blocks can be stored instead of additional instances of datablocks.

If the operating system of the secondary storage computing device 106 onwhich media agent 144 operates supports sparse files, then when mediaagent 144 creates container files 190/191/193, it can create them assparse files. A sparse file is a type of file that may include emptyspace (e.g., a sparse file may have real data within it, such as at thebeginning of the file and/or at the end of the file, but may also haveempty space in it that is not storing actual data, such as a contiguousrange of bytes all having a value of zero). Having container files190/191/193 be sparse files allows media agent 144 to free up space incontainer files 190/191/193 when blocks of data in container files190/191/193 no longer need to be stored on the storage devices. In someexamples, media agent 144 creates a new container file 190/191/193 whena container file 190/191/193 either includes 100 blocks of data or whenthe size of the container file 190 exceeds 50 MB. In other examples,media agent 144 creates a new container file 190/191/193 when acontainer file 190/191/193 satisfies other criteria (e.g., it containsfrom approx. 100 to approx. 1000 blocks or when its size exceedsapproximately 50 MB to 1 GB). In some cases, a file on which a secondarycopy operation is performed may comprise a large number of data blocks.For example, a 100 MB file may comprise 400 data blocks of size 256 KB.If such a file is to be stored, its data blocks may span more than onecontainer file, or even more than one chunk folder. As another example,a database file of 20 GB may comprise over 40,000 data blocks of size512 KB. If such a database file is to be stored, its data blocks willlikely span multiple container files, multiple chunk folders, andpotentially multiple volume folders. Restoring such files may requireaccessing multiple container files, chunk folders, and/or volume foldersto obtain the requisite data blocks.

Using Backup Data for Replication and Disaster Recovery (“LiveSynchronization”)

There is an increased demand to off-load resource intensive informationmanagement tasks (e.g., data replication tasks) away from productiondevices (e.g., physical or virtual client computing devices) in order tomaximize production efficiency. At the same time, enterprises expectaccess to readily-available up-to-date recovery copies in the event offailure, with little or no production downtime.

FIG. 2A illustrates a system 200 configured to address these and otherissues by using backup or other secondary copy data to synchronize asource subsystem 201 (e.g., a production site) with a destinationsubsystem 203 (e.g., a failover site). Such a technique can be referredto as “live synchronization” and/or “live synchronization replication.”In the illustrated embodiment, the source client computing devices 202 ainclude one or more virtual machines (or “VMs”) executing on one or morecorresponding VM host computers 205 a, though the source need not bevirtualized. The destination site 203 may be at a location that isremote from the production site 201, or may be located in the same datacenter, without limitation. One or more of the production site 201 anddestination site 203 may reside at data centers at known geographiclocations, or alternatively may operate “in the cloud.”

The synchronization can be achieved by generally applying an ongoingstream of incremental backups from the source subsystem 201 to thedestination subsystem 203, such as according to what can be referred toas an “incremental forever” approach. FIG. 2A illustrates an embodimentof a data flow which may be orchestrated at the direction of one or morestorage managers (not shown). At step 1, the source data agent(s) 242 aand source media agent(s) 244 a work together to write backup or othersecondary copies of the primary data generated by the source clientcomputing devices 202 a into the source secondary storage device(s) 208a. At step 2, the backup/secondary copies are retrieved by the sourcemedia agent(s) 244 a from secondary storage. At step 3, source mediaagent(s) 244 a communicate the backup/secondary copies across a networkto the destination media agent(s) 244 b in destination subsystem 203.

As shown, the data can be copied from source to destination in anincremental fashion, such that only changed blocks are transmitted, andin some cases multiple incremental backups are consolidated at thesource so that only the most current changed blocks are transmitted toand applied at the destination. An example of live synchronization ofvirtual machines using the “incremental forever” approach is found inU.S. Patent Application No. 62/265,339 entitled “Live Synchronizationand Management of Virtual Machines across Computing and VirtualizationPlatforms and Using Live Synchronization to Support Disaster Recovery.”Moreover, a deduplicated copy can be employed to further reduce networktraffic from source to destination. For instance, the system can utilizethe deduplicated copy techniques described in U.S. Pat. No. 9,239,687,entitled “Systems and Methods for Retaining and Using Data BlockSignatures in Data Protection Operations.”

At step 4, destination media agent(s) 244 b write the receivedbackup/secondary copy data to the destination secondary storagedevice(s) 208 b. At step 5, the synchronization is completed when thedestination media agent(s) and destination data agent(s) 242 b restorethe backup/secondary copy data to the destination client computingdevice(s) 202 b. The destination client computing device(s) 202 b may bekept “warm” awaiting activation in case failure is detected at thesource. This synchronization/replication process can incorporate thetechniques described in U.S. patent application Ser. No. 14/721,971,entitled “Replication Using Deduplicated Secondary Copy Data.”

Where the incremental backups are applied on a frequent, on-going basis,the synchronized copies can be viewed as mirror or replication copies.Moreover, by applying the incremental backups to the destination site203 using backup or other secondary copy data, the production site 201is not burdened with the synchronization operations. Because thedestination site 203 can be maintained in a synchronized “warm” state,the downtime for switching over from the production site 201 to thedestination site 203 is substantially less than with a typical restorefrom secondary storage. Thus, the production site 201 may flexibly andefficiently fail over, with minimal downtime and with relativelyup-to-date data, to a destination site 203, such as a cloud-basedfailover site. The destination site 203 can later be reversesynchronized back to the production site 201, such as after repairs havebeen implemented or after the failure has passed.

Integrating with the Cloud Using File System Protocols

Given the ubiquity of cloud computing, it can be increasingly useful toprovide data protection and other information management services in ascalable, transparent, and highly plug-able fashion. FIG. 2B illustratesan information management system 200 having an architecture thatprovides such advantages, and incorporates use of a standard file systemprotocol between primary and secondary storage subsystems 217, 218. Asshown, the use of the network file system (NFS) protocol (or any anotherappropriate file system protocol such as that of the Common InternetFile System (CIFS)) allows data agent 242 to be moved from the primarystorage subsystem 217 to the secondary storage subsystem 218. Forinstance, as indicated by the dashed box 206 around data agent 242 andmedia agent 244, data agent 242 can co-reside with media agent 244 onthe same server (e.g., a secondary storage computing device such ascomponent 106), or in some other location in secondary storage subsystem218.

Where NFS is used, for example, secondary storage subsystem 218allocates an NFS network path to the client computing device 202 or toone or more target applications 210 running on client computing device202. During a backup or other secondary copy operation, the clientcomputing device 202 mounts the designated NFS path and writes data tothat NFS path. The NFS path may be obtained from NFS path data 215stored locally at the client computing device 202, and which may be acopy of or otherwise derived from NFS path data 219 stored in thesecondary storage subsystem 218.

Write requests issued by client computing device(s) 202 are received bydata agent 242 in secondary storage subsystem 218, which translates therequests and works in conjunction with media agent 244 to process andwrite data to a secondary storage device(s) 208, thereby creating abackup or other secondary copy. Storage manager 240 can include apseudo-client manager 217, which coordinates the process by, among otherthings, communicating information relating to client computing device202 and application 210 (e.g., application type, client computing deviceidentifier, etc.) to data agent 242, obtaining appropriate NFS path datafrom the data agent 242 (e.g., NFS path information), and deliveringsuch data to client computing device 202.

Conversely, during a restore or recovery operation client computingdevice 202 reads from the designated NFS network path, and the readrequest is translated by data agent 242. The data agent 242 then workswith media agent 244 to retrieve, re-process (e.g., re-hydrate,decompress, decrypt), and forward the requested data to client computingdevice 202 using NFS.

By moving specialized software associated with system 200 such as dataagent 242 off the client computing devices 202, the illustrativearchitecture effectively decouples the client computing devices 202 fromthe installed components of system 200, improving both scalability andplug-ability of system 200. Indeed, the secondary storage subsystem 218in such environments can be treated simply as a read/write NFS targetfor primary storage subsystem 217, without the need for informationmanagement software to be installed on client computing devices 202. Asone example, an enterprise implementing a cloud production computingenvironment can add VM client computing devices 202 without installingand configuring specialized information management software on theseVMs. Rather, backups and restores are achieved transparently, where thenew VMs simply write to and read from the designated NFS path. Anexample of integrating with the cloud using file system protocols orso-called “infinite backup” using NFS share is found in U.S. PatentApplication No. 62/294,920, entitled “Data Protection Operations Basedon Network Path Information.” Examples of improved data restorationscenarios based on network-path information, including using storedbackups effectively as primary data sources, may be found in U.S. PatentApplication No. 62/297,057, entitled “Data Restoration Operations Basedon Network Path Information.”

Highly Scalable Managed Data Pool Architecture

Enterprises are seeing explosive data growth in recent years, often fromvarious applications running in geographically distributed locations.FIG. 2C shows a block diagram of an example of a highly scalable,managed data pool architecture useful in accommodating such data growth.The illustrated system 200, which may be referred to as a “web-scale”architecture according to certain embodiments, can be readilyincorporated into both open compute/storage and common-cloudarchitectures.

The illustrated system 200 includes a grid 245 of media agents 244logically organized into a control tier 231 and a secondary or storagetier 233. Media agents assigned to the storage tier 233 can beconfigured to manage a secondary storage pool 208 as a deduplicationstore, and be configured to receive client write and read requests fromthe primary storage subsystem 217, and direct those requests to thesecondary tier 233 for servicing. For instance, media agents CMA1-CMA3in the control tier 231 maintain and consult one or more deduplicationdatabases 247, which can include deduplication information (e.g., datablock hashes, data block links, file containers for deduplicated files,etc.) sufficient to read deduplicated files from secondary storage pool208 and write deduplicated files to secondary storage pool 208. Forinstance, system 200 can incorporate any of the deduplication systemsand methods shown and described in U.S. Pat. No. 9,020,900, entitled“Distributed Deduplicated Storage System,” and U.S. Pat. Pub. No.2014/0201170, entitled “High Availability Distributed DeduplicatedStorage System.”

Media agents SMA1-SMA6 assigned to the secondary tier 233 receive writeand read requests from media agents CMA1-CMA3 in control tier 231, andaccess secondary storage pool 208 to service those requests. Mediaagents CMA1-CMA3 in control tier 231 can also communicate with secondarystorage pool 208, and may execute read and write requests themselves(e.g., in response to requests from other control media agentsCMA1-CMA3) in addition to issuing requests to media agents in secondarytier 233. Moreover, while shown as separate from the secondary storagepool 208, deduplication database(s) 247 can in some cases reside instorage devices in secondary storage pool 208.

As shown, each of the media agents 244 (e.g., CMA1-CMA3, SMA1-SMA6,etc.) in grid 245 can be allocated a corresponding dedicated partition251A-251I, respectively, in secondary storage pool 208. Each partition251 can include a first portion 253 containing data associated with(e.g., stored by) media agent 244 corresponding to the respectivepartition 251. System 200 can also implement a desired level ofreplication, thereby providing redundancy in the event of a failure of amedia agent 244 in grid 245. Along these lines, each partition 251 canfurther include a second portion 255 storing one or more replicationcopies of the data associated with one or more other media agents 244 inthe grid.

System 200 can also be configured to allow for seamless addition ofmedia agents 244 to grid 245 via automatic configuration. As oneillustrative example, a storage manager (not shown) or other appropriatecomponent may determine that it is appropriate to add an additional nodeto control tier 231, and perform some or all of the following: (i)assess the capabilities of a newly added or otherwise availablecomputing device as satisfying a minimum criteria to be configured as orhosting a media agent in control tier 231; (ii) confirm that asufficient amount of the appropriate type of storage exists to supportan additional node in control tier 231 (e.g., enough disk drive capacityexists in storage pool 208 to support an additional deduplicationdatabase 247); (iii) install appropriate media agent software on thecomputing device and configure the computing device according to apre-determined template; (iv) establish a partition 251 in the storagepool 208 dedicated to the newly established media agent 244; and (v)build any appropriate data structures (e.g., an instance ofdeduplication database 247). An example of highly scalable managed datapool architecture or so-called web-scale architecture for storage anddata management is found in U.S. Patent Application No. 62/273,286entitled “Redundant and Robust Distributed Deduplication Data StorageSystem.”

The embodiments and components thereof disclosed in FIGS. 2A, 2B, and2C, as well as those in FIGS. 1A-1H, may be implemented in anycombination and permutation to satisfy data storage management andinformation management needs at one or more locations and/or datacenters.

Machine Learning-Based Data Object Storing and Retrieval

FIG. 3 is a block diagram illustrating some salient portions of aninformation management system, such as the information management system100, that stores and/or recalls data objects using machine learning,according to an illustrative embodiment of the present invention. Asillustrated in FIG. 3, a media agent 144 executing on a secondarystorage computing device 106 can include a data object usage monitor342, a data storage machine learning (ML) training system 344, and arecall ML training system 346. The secondary storage computing device106 may further include an ML model storage device 350.

The data object usage monitor 342 can be configured to monitor datausage on one or more client computing devices 102. For example, the datausage information (also referred to herein as “data usage data,” “dataobject usage data,” or “data object usage information”) can include dataobject access times (e.g., file and/or folder access times), data objectpermissions, data object ownership information, data object datapathinformation (e.g., locations in a file system associated with a logicalvolume of a client computing device 102 in which files, folders, etc.are stored), associations between applications and data objects (e.g.,information indicating which application generated a specific dataobject), data object size, data object type (e.g., a file extension),data object name information (e.g., a file or folder name), and/or thelike.

In some embodiments, as a user operates the client computing device 102,the client computing device 102 periodically sends data usageinformation to the data object usage monitor 342. For example, the dataagent 142 running on the client computing device 102 can track datausage and periodically provide the data usage information to the dataobject usage monitor 342. In other embodiments, the data object usagemonitor 342 actively requests data usage information from the clientcomputing device 102. For example, the data agent 142 running on theclient computing device 102 can track data usage, and provide the datausage information to the data object usage monitor 342 upon request bythe data object usage monitor 342. The data object usage monitor 342 canrequest the data usage information periodically (e.g., hourly, daily,weekly, etc.), after the media agent 144 receives a data object storagerequest (e.g., a data object backup request, a data object archiverequest, and/or another data object data protection operation), afterthe media agent 144 receives a data object recall request, and/or thelike.

Once obtained, the data object usage monitor 342 can store the datausage information. For example, the data object usage monitor 342 canstore the data usage information in the media agent database 152. Thedata usage information may be stored as a part of the index 153 or maybe stored separate from the index 153. In other embodiments, not shown,the data object usage monitor 342 stores the data usage information in aseparate database or index internal to or remote from the secondarystorage computing device 106.

The data storage ML training system 344 can be configured to trainand/or retrain one or more data storage ML models. For example, a datastorage ML model, once trained, may predict, given one or more inputs,what data objects should be stored (e.g., backed up, archived, and/oranother data protection operation) and/or when the storage should beperformed. Each data storage ML model may be associated with a specificclient computing device 102 and/or user. The data storage ML trainingsystem 344 can use the data usage information to train and/or retrainthe data storage ML model(s).

As an example, when a client computing device 102 is first initializedin the information management system 100, the client computing device102 may initially be configured to store data objects in a secondarystorage device 108 according to one or more storage policies, such as ina manner as described above. The client computing device 102 mayinitially be configured to store data objects in a secondary storagedevice 108 according to storage polic(ies) because a sufficient amountof data usage information may not be available to properly train a datastorage ML model associated with the recently-initially client computingdevice 102.

Thus, the data object usage monitor 342 can monitor the data usage ofthe client computing device 102 over time, storing such information inthe media agent database 152 (or in another database or index). The datastorage ML training system 344 can then retrieve the data usageinformation to begin the training process. For example, when the clientcomputing device 102 has recently been initialized, no data storage MLmodel associated with the client computing device 102 may havepreviously been trained. Accordingly, the data storage ML trainingsystem 344 can use the retrieved data usage information to train a newdata storage ML model that will be associated with the client computingdevice 102. Alternatively, if a data storage ML model associated withthe client computing device 102 has already been trained, then the datastorage ML training system 344 can retrieve the data storage ML model(e.g., from the ML model storage device 350) and retrain the datastorage ML model using the retrieved data usage information.

A user of the client computing device 102 and/or applications running onthe client computing device 102 may have a certain pattern of behaviorthat depends on a large number of variables and is thus imperceptible tohumans. For example, a user may access a set of files only during aspecific time period (e.g., during February) if the files are generatedby a first application or a second application, are stored in a firstfolder that has less than a threshold number of files (e.g., 100 files),have a certain file extension (e.g., .exe, .doc, .txt, etc.), aregreater than a certain file size (e.g., 10 kb), are less than anotherfile size (e.g., 1.4 mb), and have certain characters (e.g., “1,” “e,”and “5”) in the file name. As another example, an application running onthe client computing device 102 may generate a set of files that arenever accessed by a user operating the client computing device 102 ifthe user instructs the application to perform a certain action (e.g.,convert an image into a text document), an error occurs when theapplication attempts to perform the action, the application restarts asa result of the error, and the files are stored in a temporary folder.Thus, the patterns may be useful in that the patterns may indicate thecontext (e.g., data attributes, such as data size, ownershippermissions, the application that generated the data, the location ofthe stored data, etc., and/or other attributes that indicate the contextunder which the data is generated) under which data is accessed, is notaccessed, is accessed at certain time periods, etc.

During the training process, however, the data storage ML trainingsystem 344 may be able to derive these patterns from the data usageinformation. The data storage ML model resulting from the trainingprocess, therefore, may produce predictions that are generated based atleast in part on these derived patterns. For example, if an input isprovided to the trained data storage ML model indicating that thecurrent time is a date in March and a file generated by the firstapplication has been stored in a folder with less than 100 files, has a.txt file extension, has a file size between 10 kb and 1.4 mb, and isnamed “testfile1,” then the trained data storage ML model may produce aprediction with a certain confidence level (e.g., 80%) indicating thatthe file should be stored in a secondary storage device 108 immediately(e.g., given that the file likely, with 80% confidence, won't beaccessed until next February).

In further embodiments, the information management system 100 includes auser directory system 360 or is configured to communicate with a userdirectory system 360 external to the information management system 100.The user directory system 360 can manage user credentials for accessingcomponents of the information management system 100, such as a clientcomputing device 102. The user directory system 360 may further storemetadata associated with the user credentials, such as a time a usercredential was last used, what data object permissions are associatedwith a user credential, a flag indicating whether a user credential isstill active, and/or the like. Information identifying the usercredentials and the metadata associated with the user credentials maycollectively be referred to as “user directory information.” Similar tothe data usage information, the user directory information may includepatterns derivable by the data storage ML training system 344. Forexample, one pattern may be that data objects created by users with usercredentials that are flagged as inactive are rarely accessed. Thus, thedata storage ML training system 344 can train or retrain a data storageML model using the user directory information separately or inconjunction with the data usage information. The data storage ML modelresulting from the training process, therefore, may produce predictionsthat are generated based at least in part on the patterns derived fromthe data usage information and/or the patterns derived from the userdirectory information. For example, if an input is provided to thetrained data storage ML model indicating that a first user credential ismarked as inactive, then the trained data storage ML model may produce aprediction with a certain confidence level (e.g., 75%) indicating thatdata objects created by the user associated with the first usercredential should be stored in a secondary storage device 108 (e.g.,given that the data objects likely, with 75% confidence, will be rarelyaccessed).

Once the data storage ML model associated with the client computingdevice 102 is trained or retrained, the data storage ML training system344 can store the trained data storage ML model in the ML model storagedevice 350 in an entry associated with the client computing device 102.Alternatively or in addition, the data storage ML training system 344transmits the trained data storage ML model to the associated clientcomputing device 102 such that the client computing device 102 beginsrequesting the storage of data objects in one or more secondary storagedevices 108 according to the trained data storage ML model instead ofaccording to the storage polic(ies) because a sufficient amount of datausage information and/or user directory information may now be availableto have properly trained (e.g., accurately trained to a thresholdconfidence level) a data storage ML model. If the client computingdevice 102 has already been configured to use a data storage ML modeland the data storage ML training system 344 just completed retrainingthe data storage ML model, then the data storage ML training system 344can transmit the retrained data storage ML model to the associatedclient computing device 102 such that the client computing device 102begins requesting the storage of data objects in one or more secondarystorage devices 108 according to the retrained data storage ML modelinstead of according to the previously trained data storage ML model.Alternatively or in addition, the data storage ML training system 344can use the trained data storage ML model to identify data object(s) tostore in a secondary storage device 108 and can request the identifieddata object(s) from the client computing device 102 such that the mediaagent 144 can store the data object(s) in the secondary storage device108. Thus, the data storage ML training system 344 can improve thefunctionality of the client computing device 102 (e.g., by providing theclient computing device 102 with an updated model that should furtherimprove the ability of the client computing device 102 to reduceresponse latency and to manage memory usage).

This process can be repeated for any number of client computing devices102. Thus, the ML model storage device 350 may store a plurality oftrained data storage ML models, one or more for each client computingdevice 102.

The recall ML training system 346 can be configured to train and/orretrain one or more recall or retrieval ML models. For example, a recallML model, once trained, may predict, given one or more inputs, what dataobjects stored in a secondary storage device 108 should be recalledand/or when the recall should be performed. Each recall ML model may beassociated with a specific client computing device 102 and/or user.

The recall ML training system 346 can use the data usage information totrain and/or retrain the recall ML model(s). In particular, like thedata storage ML training system 344, the recall ML training system 346can derive patterns from the data usage information to train and/orretrain the recall ML model(s). For example, one pattern may be that auser accesses certain data objects seasonally (e.g., during a particulartime period, such as during a year's second quarter). The recall MLtraining system 346 can use time-series analysis (e.g., perform ananalysis that indicates time periods in which data objects are generallyaccessed and time periods in which the data objects are not generallyaccessed) and/or clustering techniques (e.g., a set of clusters thateach have a centroid may defined, where each cluster represents a timeperiod, and a data object corresponds to the cluster, and therefore thetime period, that has a centroid the shortest distance from the dataobject) to derive seasonality patterns and/or other patterns that can bederived from the data usage information. The recall ML model resultingfrom the training process, therefore, may produce predictions that aregenerated based at least in part on these derived patterns. For example,if data objects accessed seasonally during the year's second quarter arestored in the secondary storage device 108 (e.g., in response to aprediction made by a trained data storage ML model) and an input isprovided to the trained recall ML model indicating that the current timeis a date just prior to the start of the year's second quarter (e.g.,March 31), then the trained recall ML model may produce a predictionwith a certain confidence level (e.g., 92%) indicating that the dataobjects accessed seasonally during the year's second quarter should berecalled from the secondary storage device 108 prior to the start of thesecond quarter (e.g., by April 1) (e.g., given that the data objectslikely, with 92% confidence, will be accessed on or after April 1).

In further embodiments, the media agent 144 stores data object recallcontext information (e.g., in the media agent database 152) wheninstructions are received from a client computing device 102 to recall adata object stored in the secondary storage device 108. The data objectrecall context information can be stored in an entry associated with theclient computing device 102 that transmitted the recall instruction andcan include a time that the data object recall was requested, other dataobjects co-located with the requested data object (e.g. other filesstored in the same folder as the requested file, other files of the samefile type as the requested file, other emails in a chain of emails thatincludes the requested email, other files generated by the applicationthat generated the requested file, etc.), a datapath in the file systemto which the requested data object will be stored, and/or the like.

Similar to the data usage information, a set of data object recallcontext information (e.g., data object recall context information storedas a result of multiple data object recall requests) may includepatterns derivable by the recall ML training system 346. For example,one pattern may be that when a user requests the recall of one dataobject initially stored in a first folder (e.g., a photos folder), theuser generally also requests the recall of other data objects stored inthe same first folder (e.g., other photos in the photos folder). Anotherpattern may be that when a user requests the recall of one email in anemail chain, the user generally also requests the recall of other emailsin the same email chain. Thus, the recall ML training system 346 cantrain or retrain a recall ML model using the data object recall contextinformation separately or in conjunction with the data usageinformation. The recall ML model resulting from the training process,therefore, may produce predictions that are generated based at least inpart on the patterns derived from the data usage information and/or thepatterns derived from the data object recall context information. Forexample, if the data objects in a first folder have been stored in thesecondary storage device 108 and an input is provided to the trainedrecall ML model indicating that a client computing device 102 isrequesting the recall of a first data object in the first folder, thenthe trained recall ML model may produce a prediction with a certainconfidence level (e.g., 87%) indicating that other data objects in thefirst folder that have been stored in the secondary storage device 108should be recalled from the secondary storage device 108 after therequested first data object is recalled (e.g., given that a recallrequest for the other data objects likely, with 87% confidence, will bereceived).

Once the recall ML model associated with the client computing device 102is trained or retrained, the recall ML training system 346 can store thetrained recall ML model in the ML model storage device 350 in an entryassociated with the client computing device 102. Alternatively or inaddition, the recall ML training system 346 transmits the trained recallML model to the associated client computing device 102 such that theclient computing device 102 begins recalling data objects according tothe trained recall ML model. If the client computing device 102 hasalready been configured to use a recall ML model and the recall MLtraining system 346 just completed retraining the recall ML model, thenthe recall ML training system 346 can transmit the retrained recall MLmodel to the associated client computing device 102 such that the clientcomputing device 102 begins recalling data objects according to theretrained recall ML model instead of the previously trained recall MLmodel. Alternatively or in addition, the recall ML training system 346can use the trained recall ML model to proactively recall data objectsand provide the recalled data objects to the client computing device102. Thus, the recall ML training system 346 can improve thefunctionality of the client computing device 102 (e.g., by providing theclient computing device 102 with an updated model that should furtherimprove the ability of the client computing device 102 to reduceresponse latency and to manage memory usage).

This process can be repeated for any number of client computing devices102. Thus, the ML model storage device 350 may store a plurality oftrained recall ML models, one or more for each client computing device102.

The data storage ML training system 344 and/or the recall ML trainingsystem 346 can train and/or retrain the ML models periodically, afternew data usage information is obtained, after new user directoryinformation is obtained, after new data object recall contextinformation is obtained, and/or the like.

The data storage ML training system 344 described herein can be used totrain any type of data storage ML model. For example, the data storageML training system 344 can train a backup ML model (e.g., an ML modelthat predicts which data objects should be backed up and/or when toperform the backup), an archive ML model (e.g., an ML model thatpredicts which data objects should be archived and/or when to performthe archiving), and/or another other data protection ML model (e.g., anyother ML model that predicts which data objects on which a dataprotection operation should be performed and/or when to perform the dataprotection operation).

FIG. 4A illustrates a block diagram showing the operations performed tostore a data object in a secondary storage device 108 according to astorage policy. For example, the operations depicted in FIG. 4A may beperformed before a sufficient amount of data has been obtained toproperly train a data storage ML model (e.g., soon after a clientcomputing device 102 has been initialized within the informationmanagement system 100).

As illustrated in FIG. 4A, a client computing device 102 identifies adata object to store using a storage policy at (1). For example, theclient computing device 102 may be configured by the storage manager 140with the storage policy. As an illustrative example, the storage policymay indicate that data objects not accessed for a certain time period(e.g., 90 days) should be stored in the secondary storage device 108,and here the identified data object may not have been accessed for thetime period.

In response to identifying the data object to store, the clientcomputing device 102 transmits the data object to the media agent 144for storage at (2). The media agent 144 can process the received dataobject to generate a secondary copy of the data object (e.g., convertthe data object into an object in a secondary copy format), and storethe secondary copy of the data object in the secondary storage device108 at (3).

FIG. 4B illustrates a block diagram showing the operations performed totrain a data storage ML model for determining which data objects tostore in a secondary storage device 108 and/or when to perform thestoring. As illustrated in FIG. 4B, the data object usage monitor 342monitors data object usage on the client computing device 102 at (1).For example, the data object usage monitor 342 can request data objectusage data from the client computing device 102 or the client computingdevice 102 can proactively provide data object usage data to the dataobject usage monitor 342. The data object usage monitor 342 can thenstore the data object usage data in the media agent database 152 at (2).

At a later time or immediately after the data object usage monitor 342stores the data object usage data, the data storage ML training system344 can retrieve the data object usage data from the media agentdatabase 152 at (3). Optionally, the data storage ML training system 344can also retrieve user directory information from the user directorysystem 360 at (4).

If the client computing device 102 is already associated with a datastorage ML model, then the data storage ML training system 344 canretrieve the associated data storage ML model from the ML model storagedevice 350 at (5). Otherwise, if the client computing device 102 is notalready associated with a data storage ML model, then the data storageML training system 344 can begin the process of training a new datastorage ML model for association with the client computing device 102.

Once the above-described information has been gathered, the data storageML training system 344 can train or retrain the data storage ML modelusing the retrieved data object usage data and/or the user directoryinformation at (6). For example, the data storage ML training system 344can train or retrain the data storage ML model such that the datastorage ML model predicts what data objects to store in the secondarystorage device 108 and/or when to perform the storage when the datastorage ML model is provided with one or more inputs (e.g., a currenttime, an identification of a file generated by a particular application,the age of a file, a name of a file, a size of a file, a file type,information identifying an active or inactive status of usercredentials, etc.).

The data storage ML training system 344 can then store the trained (orretrained) data storage ML model in the ML model storage device 350 at(7). Optionally, the data storage ML training system 344 can transmitthe trained (or retrained) data storage ML model to the client computingdevice 102 at (8). The client computing device 102 can then use thetrained data storage ML model instead of storage polic(ies) to determinewhich data objects to store in the secondary storage device 108 and/orwhen to perform the storage, as described below with respect to FIG. 4C.

FIG. 4C illustrates a block diagram showing the operations performed tostore a data object in a secondary storage device 108 according to adata storage ML model. For example, the operations depicted in FIG. 4Cmay be performed after a data storage ML model has been trained and aclient computing device 102 has been configured to use the data storageML model.

As illustrated in FIG. 4C, a client computing device 102 identifies adata object to store using the data storage ML model instead of thestorage policy at (1). For example, by receiving the data storage MLmodel, the client computing device 102 may no longer perform operationsaccording to the storage policy. Alternatively, the client computingdevice 102 may prioritize the predictions of the data storage ML modelover the storage policy. If the predictions of the data storage ML modeland the actions defined by the storage policy conflict, then the clientcomputing device 102 may perform operations corresponding to the datastorage ML model predictions. If the predictions of the data storage MLmodel and the actions defined by the storage policy do not conflict,then the client computing device 102 may perform operationscorresponding to the data storage ML model predictions and actionsdefined by the storage policy.

In response to identifying the data object to store, the clientcomputing device 102 transmits the data object to the media agent 144for storage at (2). The media agent 144 can process the received dataobject to generate a secondary copy of the data object (e.g., convertthe data object into an object in a secondary copy format), and storethe secondary copy of the data object in the secondary storage device108 at (3).

As described above, the data storage ML training system 344 optionallytransmits the data storage ML model to the client computing device 102.In embodiments in which the data storage ML model is not transmitted tothe client computing device 102, the media agent 144 may handle theoperations performed to identify which data objects to store in thesecondary storage device 108 and/or when to perform the storage, asdescribed in greater detail below with respect to FIG. 4D.

FIG. 4D illustrates another block diagram showing the operationsperformed to store a data object in a secondary storage device 108according to a data storage ML model. For example, the operationsdepicted in FIG. 4D may be performed after a data storage ML model hasbeen trained or retrained.

As illustrated in FIG. 4D, a media agent 144 identifies a data object tostore using the data storage ML model at (1). For example, the mediaagent 144 may make the identification instead of the client computingdevice 102, which may be relying on one or more storage policies to makesuch an identification. The media agent 144 may use the content storedin the media agent database 152 (e.g., the index 153, the data objectusage data, etc.) and/or data object information (e.g., file metadata,folder metadata, etc.) received from the client computing device 102 toprovide the data storage ML model with one or more inputs such that thedata storage ML model can produce the prediction that identifies thatthe data object should be stored in the secondary storage device 108.Alternatively, the media agent 144 may prioritize the predictions of thedata storage ML model over a storage policy with which the clientcomputing device 102 is configured. If the predictions of the datastorage ML model and the actions defined by the storage policy conflict,then the media agent 144 may perform operations corresponding to thedata storage ML model predictions. If the predictions of the datastorage ML model and the actions defined by the storage policy do notconflict, then the media agent 144 may perform operations correspondingto the data storage ML model predictions and actions defined by thestorage policy.

In response to identifying the data object to store, the media agent 144requests a primary copy of the data object from the client computingdevice 102. In response, the client computing device 102 transmits thedata object to the media agent 144 for storage at (3). The media agent144 can process the received data object to generate a secondary copy ofthe data object (e.g., convert the data object into an object in asecondary copy format), and store the secondary copy of the data objectin the secondary storage device 108 at (4).

FIG. 5A illustrates a block diagram showing the operations performed torecall a data object in response to a user request. For example, theoperations depicted in FIG. 5A may be performed before a sufficientamount of data has been obtained to properly train a recall ML model(e.g., soon after a client computing device 102 has been initializedwithin the information management system 100).

As illustrated in FIG. 5A, the client computing device 102 receives aselection of a data object to recall at (1). For example, the clientcomputing device 102 may display a user interface depicting a list offiles and/or folders, and a user may select one of the files or foldersto access. The selected file or folder may be stored in the secondarystorage device 108, however, and thus the client computing device 102initiates the process to perform a recall of the selected file or folderfrom the secondary storage device 108.

In response to receiving the selection of the data object, the clientcomputing device 102 transmits a request for the data object to themedia agent 144 at (2). The media agent 144 can store data object recallcontext information corresponding to the data object recall request inthe media agent database 152 at (3). For example, the media agent 144can obtain the data object recall context information from the clientcomputing device 102 in response to receiving the data object recallrequest, as part of the data object recall request, by analyzing theindex 153 stored in the media agent database 152 (e.g., using the index153 to identify data objects co-located with the requested data object),and/or the like.

The media agent 144, before, during, or after storing the data objectrecall context information, can request a secondary copy of the dataobject from the secondary storage device 108 at (4). In response, thesecondary storage device 108 can transmit the secondary copy of the dataobject to the media agent 144 at (5).

The media agent 144 may then process the secondary copy of the dataobject to generate a primary copy of the data object (e.g., convert thesecondary copy of the data object from the secondary copy format to theoriginal format), and transmit the primary copy of the requested dataobject to the client computing device 102 at (6).

FIG. 5B illustrates a block diagram showing the operations performed totrain a recall ML model for determining which data objects to recalland/or when to perform the recall. As illustrated in FIG. 5B, the recallML training system 346 retrieves stored data object recall contextinformation and/or data object usage data from the media agent database152 at (1). The data object recall context information can correspond toinformation obtained as a result of a plurality of data object recallrequests.

If the client computing device 102 is already associated with a recallML model, then the recall ML training system 346 can retrieve theassociated recall ML model from the ML model storage device 350 at (2).Otherwise, if the client computing device 102 is not already associatedwith a recall ML model, then the recall ML training system 346 can beginthe process of training a new recall ML model for association with theclient computing device 102.

Once the above-described information has been gathered, the recall MLtraining system 346 can train or retrain the recall ML model using theretrieved data object recall context information and/or the retrieveddata object usage data at (3). For example, the recall ML trainingsystem 346 can train or retrain the recall ML model such that the recallML model predicts what data objects to recall and/or when to perform therecall when the recall ML model is provided with one or more inputs(e.g., a current time, an identification of the reception of a requestto recall a file and/or an identification of files co-located with therequested file, an identification of an application that generated arequested file, the age of a requested file, a name of a requested file,a size of a requested file, a file type of the requested file, adatapath of a requested file, etc.).

The recall ML training system 346 can then store the trained (orretrained) recall ML model in the ML model storage device 350 at (4).Optionally, the recall ML training system 346 can transmit the trained(or retrained) recall ML model to the client computing device 102 at(5). The client computing device 102 can then use the trained recall MLmodel to determine which data objects to recall and/or when to performthe recall instead of relying solely on user requests to access dataobjects, as described below with respect to FIG. 5C.

FIG. 5C illustrates a block diagram showing the operations performed torecall a data object according to a recall ML model. For example, theoperations depicted in FIG. 5C may be performed after the clientcomputing device 102 receives a trained or retrained recall ML modelfrom the recall ML training system 346.

As illustrated in FIG. 5C, the client computing device 102 identifies adata object to recall using the recall ML model at (1). The recall MLmodel may predict that the data object should be recalled because it islikely, with a certain confidence level, that the data object will berequested by the user of the client computing device 102 in the nearfuture (e.g., within the next minute, within the next hour, within thenext day, within the next week, etc.). The client computing device 102can execute the recall ML model periodically and/or when there is achange to the file system (e.g., a data object is added, a data objectis deleted, a data object is modified, etc.) in order to identify dataobjects to recall. The client computing device 102 may obtain inputs toprovide to the recall ML model based on, for example, the current time,information associated with the change to the file system, and/or thelike.

In response to identifying the data object to recall, the clientcomputing device 102 transmits a request for the data object to themedia agent 144 at (2). The media agent 144 can store data object recallcontext information corresponding to the data object recall request inthe media agent database 152 at (3). For example, the media agent 144can obtain the data object recall context information from the clientcomputing device 102 in response to receiving the data object recallrequest, as part of the data object recall request, by analyzing theindex 153 stored in the media agent database 152 (e.g., using the index153 to identify data objects co-located with the requested data object),and/or the like.

The media agent 144, before, during, or after storing the data objectrecall context information, can request a secondary copy of the dataobject from the secondary storage device 108 at (4). In response, thesecondary storage device 108 can transmit the secondary copy of the dataobject to the media agent 144 at (5).

The media agent 144 may then process the secondary copy of the dataobject to generate a primary copy of the data object (e.g., convert thesecondary copy of the data object from the secondary copy format to theoriginal format), and transmit the primary copy of the requested dataobject to the client computing device 102 at (6).

As described above, the recall ML training system 346 optionallytransmits the recall ML model to the client computing device 102. Inembodiments in which the recall ML model is not transmitted to theclient computing device 102, the media agent 144 may handle theproactive recall operations (e.g., operations performed without userinput or a user request for the recalled data object(s)), as describedin greater detail below with respect to FIG. 5D.

FIG. 5D illustrates another block diagram showing the operationsperformed to recall a data object according to a recall ML model. Forexample, the operations depicted in FIG. 5D may be performed after arecall ML model is trained or retrained.

As illustrated in FIG. 5D, the media agent 144 identifies a data objectto recall using the recall ML model at (1). The recall ML model maypredict that the data object should be recalled because it is likely,with a certain confidence level, that the data object will be requestedby the user of the client computing device 102 in the near future (e.g.,within the next minute, within the next hour, within the next day,within the next week, etc.). The media agent 144 can execute the recallML model periodically and/or when notified by the client computingdevice 102 of a change to the file system of the client computing device102 in order to identify data objects to recall. The media agent 144 mayobtain inputs to provide to the recall ML model by retrievinginformation from the index 153, by obtaining data object metadata fromthe client computing device 102, by obtaining information associatedwith the change to the file system, etc.

In response to identifying the data object to recall, the media agent144 can request a secondary copy of the data object from the secondarystorage device 108 at (2). In response, the secondary storage device 108can transmit the secondary copy of the data object to the media agent144 at (3).

The media agent 144 may then process the secondary copy of the dataobject to generate a primary copy of the data object (e.g., convert thesecondary copy of the data object from the secondary copy format to theoriginal format), and transmit the primary copy of the requested dataobject to the client computing device 102 at (4).

FIG. 6 depicts some salient operations of a method 600 for training adata storage ML model according to an illustrative embodiment of thepresent invention. The method 600 may be implemented by the media agent144 (e.g., the data storage ML training system 344). The method 600starts at block 602.

At block 604, data object usage data is retrieved. For example, the dataobject usage data can be retrieved from the client computing device 102or the media agent database 152.

At block 606, user directory information is retrieved. For example, theuser directory information can be retrieved from the user directorysystem 360, which may be internal or external to the informationmanagement system 100.

At block 608, a data storage ML model is trained using the retrieveddata object usage data and/or the retrieved user directory information.The trained data storage ML model may be structured to predict, given aset of inputs, what data objects to store in a secondary storage device108 and/or when to perform the storage.

At block 610, the data storage ML model is transmitted to a clientcomputing device such that the client computing device uses the datastorage ML model instead of a storage policy to determine which dataobjects to store in a secondary storage device 108 and/or when toperform the storage. For example, the client computing device 102 mayinitially be configured with the storage policy and may store dataobjects in one or more secondary storage devices 108 according to thestorage policy. Once a sufficient amount of data object usage dataassociated with the client computing device 102 can be obtained, then adata storage ML model associated with the client computing device 102can be trained and be used to replace the storage policy. Replacing thestorage policy with the data storage ML model may reduce the responsedelay of the client computing device 102 and/or may reduce memory usagefor at least the reasons discussed above. After the data storage MLmodel is transmitted to the client computing device, the method 600ends, as shown at block 612.

FIG. 7 depicts some salient operations of a method 700 for training andusing a data storage ML model according to an illustrative embodiment ofthe present invention. The method 700 may be implemented by the mediaagent 144 (e.g., the data storage ML training system 344). The method700 starts at block 702.

At block 704, data object usage data is retrieved. For example, the dataobject usage data can be retrieved from the client computing device 102or the media agent database 152.

At block 706, user directory information is retrieved. For example, theuser directory information can be retrieved from the user directorysystem 360, which may be internal or external to the informationmanagement system 100.

At block 708, a data storage ML model is trained using the retrieveddata object usage data and/or the retrieved user directory information.The trained data storage ML model may be structured to predict, given aset of inputs, what data objects to store in a secondary storage device108 and/or when to perform the storage.

At block 710, a data object is identified to store (e.g., in a secondarystorage device 108) using the data storage ML model. For example, themedia agent 144 may use the content stored in the media agent database152 (e.g., the index 153, the data object usage data, etc.) and/or dataobject information (e.g., file metadata, folder metadata, etc.) receivedfrom the client computing device 102 to provide the data storage MLmodel with one or more inputs such that the data storage ML model canproduce the prediction that identifies that the data object should bestored in the secondary storage device 108.

At block 712, a primary copy of the identified data object is requestedfrom a client computing device. For example, the primary copy of theidentified data object is requested such that the media agent 144 canstore a secondary copy of the identified data object in the secondarystorage device 108.

At block 714, the primary copy of the identified data object is receivedfrom the client computing device. Alternatively, the client computingdevice transmits the primary copy of the identified data object toanother device (e.g., the storage manager 140) and the other deviceforwards the primary copy to the media agent 144.

At block 716, a secondary copy of the identified data object is storedin a secondary storage device. For example, the media agent 144 canprocess the primary copy of the identified data object to form thesecondary copy of the identified data object. After the secondary copyof the identified data object is stored, the method 700 ends, as shownat block 718.

FIG. 8 depicts some salient operations of a method 800 for training arecall ML model according to an illustrative embodiment of the presentinvention. The method 800 may be implemented by the media agent 144(e.g., the recall ML training system 346). The method 800 starts atblock 802.

At block 804, data object recall context information is retrieved. Forexample, the data object recall context information may be generatedand/or obtained from the client computing device 102 by the media agent144.

At block 806, data object usage data is retrieved. For example, the dataobject usage data can be retrieved from the client computing device 102or the media agent database 152.

At block 808, a recall ML model is trained using the retrieved dataobject recall context information and/or the retrieved data object usagedata. The trained recall ML model may be structured to predict, given aset of inputs, what data objects to recall from the secondary storagedevice 108 and/or when to perform the recall.

At block 810, the recall ML model is transmitted to a client computingdevice such that the client computing device uses the recall ML model todetermine which data objects to recall and/or when to perform the recallinstead of relying on the receipt of user requests for access to dataobjects stored in one or more secondary storage devices 108. Forexample, the client computing device 102 may initially not be configuredwith any policies that define what data objects to recall and/or when toperform the recall. Once a sufficient amount of data object usage dataassociated with the client computing device 102 can be obtained, then arecall ML model associated with the client computing device 102 can betrained and be used by the client computing device 102. Replacing theabsence of a policy with the recall ML model may reduce the responsedelay of the client computing device 102 and/or may reduce memory usagefor at least the reasons discussed above. After the recall ML model istransmitted to the client computing device, the method 800 ends, asshown at block 812.

FIG. 9 depicts some salient operations of a method 900 for training andusing a recall ML model according to an illustrative embodiment of thepresent invention. The method 900 may be implemented by the media agent144 (e.g., the recall ML training system 346). The method 900 starts atblock 902.

At block 904, data object recall context information is retrieved. Forexample, the data object recall context information may be generatedand/or obtained from the client computing device 102 by the media agent144.

At block 906, data object usage data is retrieved. For example, the dataobject usage data can be retrieved from the client computing device 102or the media agent database 152.

At block 908, a recall ML model is trained using the retrieved dataobject recall context information and/or the retrieved data object usagedata. The trained recall ML model may be structured to predict, given aset of inputs, what data objects to recall from the secondary storagedevice 108 and/or when to perform the recall.

At block 910, a data object to recall is identified using the recall MLmodel. For example, the media agent 144 may receive a request from theclient computing device 102 to recall a first data object. The mediaagent 144 can then provide an identification of the first data object tothe recall ML model as an input, and the recall ML model may predictthat the user or client computing device 102 may request the recall ofanother data object in the near future.

At block 912, a secondary copy of the identified data object isretrieved from a secondary storage device. The media agent 144 may thenprocess the secondary copy of the data object to generate a primary copyof the data object.

At block 914, the identified data object is then transmitted to theclient computing device. In this way, the media agent 144 proactivelyrecalls a data object and provides the recalled data object to theclient computing device before receiving any request to perform thisoperation. After the identified data object is transmitted to the clientcomputing device, the method 900 ends, as shown at 916.

In regard to the figures described herein, other embodiments arepossible within the scope of the present invention, such that theabove-recited components, steps, blocks, operations, and/ormessages/requests/queries/instructions are differently arranged,sequenced, sub-divided, organized, and/or combined. In some embodiments,a different component may initiate or execute a given operation. Forexample, in some embodiments, a client computing device 102 or aseparate training system within the information management system 100(not shown) can perform the training described herein with respect tothe data storage ML training system 344 and/or the recall ML trainingsystem 346. As another example, a separate computing device (not shown)can execute data storage ML models and/or recall ML models. As a resultof executing an ML model (e.g., applying one or more inputs to an MLmodel to produce a prediction), the separate computing device cantransmit corresponding instructions to the media agent 144. Suchinstructions can include an identification of data object(s) to store ina secondary storage device 108 and/or an identification of dataobject(s) to recall or restore from a secondary storage device 108. Themedia agent 144 can then request primary copies of the data object fromthe client computing device 102 for storage in the secondary storagedevice 108 (if receiving instructions to store data object(s)) and/orretrieve the identified data objects from the secondary storage device108 for transmission to the client computing device 102 (if receivinginstructions to recall data object(s)). Alternatively, the separatecomputing device can transmit corresponding instructions to the clientcomputing device 102. The client computing device 102 can then instructthe media agent 144 as described herein to store and/or recall dataobject(s).

Example Embodiments

Some example enumerated embodiments of the present invention are recitedin this section in the form of methods, systems, and non-transitorycomputer-readable media, without limitation.

In one embodiment, a networked information management system comprises aclient computing device having one or more first hardware processors,where the client computing device is configured with firstcomputer-executable instructions that, when executed, cause the clientcomputing device to store one or more data objects in a secondarystorage device according to a storage policy at a first time. Thenetworked information management system further comprises one or morecomputing devices in communication with the client computing device,where the one or more computing devices each have one or more secondhardware processors, where the one or more computing devices areconfigured with second computer-executable instructions that, whenexecuted, cause the one or more computing devices to: retrieve dataobject usage data associated with the client computing device; train adata storage machine learning (ML) model using the data object usagedata; and transmit the data storage ML model to the client computingdevice such that the client computing device uses the data storage MLmodel instead of the storage policy to determine which of the one ormore data objects to store in the secondary storage device at a secondtime after the first time.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thesecond computer-executable instructions, when executed, further causethe one or more computing devices to: retrieve user directoryinformation from a user directory system, where the user directoryinformation comprises an indication of an active or inactive status ofone or more user credentials, and train the data storage ML model usingthe data object usage data and the user directory information; where thesecond computer-executable instructions, when executed, further causethe one or more computing devices to: retrieve second data object usagedata associated with the client computing device, retrieve the datastorage ML model, retrain the data storage ML model using the seconddata object usage data, and transmit the retrained data storage ML modelto the client computing device such that the client computing deviceuses the retrained data storage ML model instead of the data storage MLmodel to determine which of the one or more data objects to store in thesecondary storage device at a third time after the second time; wherethe second computer-executable instructions, when executed, furthercause the one or more computing devices to: obtain a request to store inthe secondary storage device a first data object in the one or more dataobjects from the client computing device, where the client computingdevice generates the request in response to a prediction produced by thedata storage ML model, process the first data object to form a secondarycopy of the first data object, and store the secondary copy of the firstdata object in the secondary storage device; where the data storage MLmodel generates a prediction, with an associated confidence level,identifying a first data object in the one or more data objects to storein the secondary storage device at the second time after the first timein response to one or more inputs; where the one or more inputs compriseat least one of a current time, an identification of a second dataobject in the one or more data objects generated by a first applicationrunning on the client computing device, an age of the second dataobject, a name of the second data object, a size of the second dataobject, a data object type of the second data object, or informationidentifying an active or inactive status of one or more usercredentials; where the second computer-executable instructions, whenexecuted, further cause the one or more computing devices to train thedata storage ML model by deriving patterns from the data object usagedata; where the data object usage data comprises at least one of dataobject access times, data object permissions, data object ownershipinformation, data object datapath information, information indicatingwhich application running on the client computing device generated adata object, data object size, data object type, or data object nameinformation; and where the one or more data objects comprise at leastone of a file, a folder, a directory, a file system volume, a datablock, or an extent.

In another embodiment, a computer-implemented comprises: retrieving dataobject usage data associated with a client computing device, the clientcomputing device having one or more first hardware processors, where theclient computing device is configured with computer-executableinstructions that, when executed, cause the client computing device tostore one or more data objects in a secondary storage device accordingto a storage policy at a first time; training a data storage machinelearning (ML) model using the data object usage data; and transmittingthe data storage ML model to the client computing device such that theclient computing device uses the data storage ML model instead of thestorage policy to determine which of the one or more data objects tostore in the secondary storage device at a second time after the firsttime.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where training a datastorage ML model further comprises: retrieving user directoryinformation from a user directory system, where the user directoryinformation comprises an indication of an active or inactive status ofone or more user credentials, and training the data storage ML modelusing the data object usage data and the user directory information;where the computer-implemented method further comprises retrievingsecond data object usage data associated with the client computingdevice, retrieving the data storage ML model, retraining the datastorage ML model using the second data object usage data, andtransmitting the retrained data storage ML model to the client computingdevice such that the client computing device uses the retrained datastorage ML model instead of the data storage ML model to determine whichof the one or more data objects to store in the secondary storage deviceat a third time after the second time; where the computer-implementedmethod further comprises: receiving a request to store in the secondarystorage device a first data object in the one or more data objects fromthe client computing device, where the client computing device generatesthe request in response to a prediction produced by the data storage MLmodel, processing the first data object to form a secondary copy of thefirst data object, and storing the secondary copy of the first dataobject in the secondary storage device; where the data storage ML modelgenerates a prediction, with an associated confidence level, identifyinga first data object in the one or more data objects to store in thesecondary storage device at the second time after the first time inresponse to one or more inputs; where the one or more inputs comprise atleast one of a current time, an identification of a second data objectin the one or more data objects generated by a first application runningon the client computing device, an age of the second data object, a nameof the second data object, a size of the second data object, a dataobject type of the second data object, or information identifying anactive or inactive status of one or more user credentials; wheretraining a data storage ML model further comprises training the datastorage ML model by deriving patterns from the data object usage data;where the data object usage data comprises at least one of data objectaccess times, data object permissions, data object ownershipinformation, data object datapath information, information indicatingwhich application running on the client computing device generated adata object, data object size, data object type, or data object nameinformation; and where the one or more data objects comprise at leastone of a file, a folder, a directory, a file system volume, a datablock, or an extent.

In another embodiment, a networked information management systemcomprises a client computing device having one or more first hardwareprocessors, where the client computing device is configured with firstcomputer-executable instructions that, when executed, cause the clientcomputing device to store one or more data objects in a secondarystorage device according to a storage policy at a first time. Thenetworked information management system further comprises one or morecomputing devices in communication with the client computing device,where the one or more computing devices each have one or more secondhardware processors, where the one or more computing devices areconfigured with second computer-executable instructions that, whenexecuted, cause the one or more computing devices to: retrieve dataobject usage data associated with the client computing device; train adata storage machine learning (ML) model using the data object usagedata; and transmit the data storage ML model to the client computingdevice such that the client computing device uses at least one of thedata storage ML model or the storage policy to determine which of theone or more data objects to store in the secondary storage device at asecond time after the first time.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thesecond computer-executable instructions, when executed, further causethe one or more computing devices to: retrieve second data object usagedata associated with the client computing device, retrieve the datastorage ML model, retrain the data storage ML model using the seconddata object usage data, and transmit the retrained data storage ML modelto the client computing device such that the client computing deviceuses at least one of the retrained data storage ML model or the storagepolicy instead of the data storage ML model to determine which of theone or more data objects to store in the secondary storage device at athird time after the second time.

In another embodiment, a networked information management systemcomprises a client computing device having one or more first hardwareprocessors, the client computing device configured to execute a firstapplication that generated one or more data objects. The networkedinformation management system further comprises one or more computingdevices in communication with the client computing device, where the oneor more computing devices each have one or more second hardwareprocessors, where the one or more computing devices are configured withcomputer-executable instructions that, when executed, cause the one ormore computing devices to: retrieve data object usage recall contextinformation associated with the client computing device; train a recallmachine learning (ML) model using the data object usage recall contextinformation; and transmit the recall ML model to the client computingdevice such that the client computing device uses the recall ML model todetermine which of the one or more data objects to recall from asecondary storage device at a first time.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to: retrieve data object usage data, and trainthe recall ML model using the data object usage recall contextinformation and the data object usage data; where the data object usagedata comprises at least one of data object access times, data objectpermissions, data object ownership information, data object datapathinformation, information indicating which application running on theclient computing device generated a data object, data object size, dataobject type, or data object name information; where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to: retrieve second data object usage recallcontext information associated with the client computing device,retrieve the recall ML model, retrain the recall ML model using thesecond data object usage recall context information, and transmit theretrained recall ML model to the client computing device such that theclient computing device uses the retrained recall ML model instead ofthe recall ML model to determine which of the one or more data objectsto recall at a second time after the first time; where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to: obtain a request to recall a first dataobject in the one or more data objects from the client computing device,where the client computing device generates the request in response to aprediction produced by the recall ML model, retrieve a secondary copy ofthe first data object from the secondary storage device, process thesecondary copy of the first data object to form a primary copy of thefirst data object, and transmit the primary copy of the first dataobject to the client computing device; where the recall ML modelgenerates a prediction, with an associated confidence level, identifyinga first data object in the one or more data objects to recall at thefirst time in response to one or more inputs; where the one or moreinputs comprise at least one of a current time, an identification of areception of a request to recall a second data object in the one or moredata objects that is co-located with the first data object, anidentification of a third data object in the one or more data objectsgenerated by the first application, an age of the third data object, aname of the third data object, a size of the third data object, a dataobject type of the third data object, or a datapath of the third dataobject; where the computer-executable instructions, when executed,further cause the one or more computing devices to train the recall MLmodel by deriving patterns from the data object usage recall contextinformation; where the data object usage recall context informationcomprises at least one of a time that a recall for a first data objectin the one or more data objects was requested, other data objects in theone or more data objects co-located with the requested first dataobject, or a datapath in a file system of the client computing device towhich the requested first data object will be stored; and where the oneor more data objects comprise at least one of a file, a folder, adirectory, a file system volume, a data block, or an extent.

In another embodiment, a computer-implemented method comprises:retrieving, by one or more computing devices configured to managetransmission of data between a client computing device and a secondarystorage device, data object usage recall context information associatedwith the client computing device, the client computing device configuredto execute a first application that generated one or more data objects;training a recall machine learning (ML) model using the data objectusage recall context information; and transmitting the recall ML modelto the client computing device such that the client computing deviceuses the recall ML model to determine which of the one or more dataobjects to recall from the secondary storage device at a first time.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where training a recallML model further comprises: retrieving data object usage data, andtraining the recall ML model using the data object usage recall contextinformation and the data object usage data; where the data object usagedata comprises at least one of data object access times, data objectpermissions, data object ownership information, data object datapathinformation, information indicating which application running on theclient computing device generated a data object, data object size, dataobject type, or data object name information; where thecomputer-implemented method further comprises: retrieving second dataobject usage recall context information associated with the clientcomputing device, retrieving the recall ML model, retraining the recallML model using the second data object usage recall context information,and transmitting the retrained recall ML model to the client computingdevice such that the client computing device uses the retrained recallML model instead of the recall ML model to determine which of the one ormore data objects to recall at a second time after the first time; wherethe computer-implemented method further comprises: obtaining a requestto recall a first data object in the one or more data objects from theclient computing device, where the client computing device generates therequest in response to a prediction produced by the recall ML model,retrieving a secondary copy of the first data object from the secondarystorage device, processing the secondary copy of the first data objectto form a primary copy of the first data object, and transmitting theprimary copy of the first data object to the client computing device;where the recall ML model generates a prediction, with an associatedconfidence level, identifying a first data object in the one or moredata objects to recall at the first time in response to one or moreinputs; where the one or more inputs comprise at least one of a currenttime, an identification of a reception of a request to recall a seconddata object in the one or more data objects that is co-located with thefirst data object, an identification of a third data object in the oneor more data objects generated by the first application, an age of thethird data object, a name of the third data object, a size of the thirddata object, a data object type of the third data object, or a datapathof the third data object; and where training the recall ML model furthercomprises training the recall ML model by deriving patterns from thedata object usage recall context information.

In another embodiment, a networked information management systemcomprises a client computing device having one or more first hardwareprocessors, the client computing device configured to execute a firstapplication that generated one or more data objects. The networkedinformation management system further comprises one or more computingdevices in communication with the client computing device, where the oneor more computing devices each have one or more second hardwareprocessors, where the one or more computing devices are configured withcomputer-executable instructions that, when executed, cause the one ormore computing devices to: retrieve data object usage recall contextinformation associated with the client computing device; train a recallmachine learning (ML) model using the data object usage recall contextinformation; identify a first data object in the one or more dataobjects to recall from a secondary storage device at a first time usingthe recall ML model; retrieve a secondary copy of the first data objectfrom the secondary storage device; where process the secondary copy ofthe first data object to form a primary copy of the first data object;and where transmit the primary copy of the first data object to theclient computing device at the first time.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause the oneor more computing devices to: retrieve second data object usage recallcontext information associated with the client computing device,retrieve the recall ML model, retrain the recall ML model using thesecond data object usage recall context information, and identify asecond data object in the one or more data objects to recall from thesecondary storage device at a second time after the first time using theretrained recall ML model.

In other embodiments, a system or systems may operate according to oneor more of the methods and/or computer-readable media recited in thepreceding paragraphs. In yet other embodiments, a method or methods mayoperate according to one or more of the systems and/or computer-readablemedia recited in the preceding paragraphs. In yet more embodiments, acomputer-readable medium or media, excluding transitory propagatingsignals, may cause one or more computing devices having one or moreprocessors and non-transitory computer-readable memory to operateaccording to one or more of the systems and/or methods recited in thepreceding paragraphs.

Terminology

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. Two or more components of a system can be combinedinto fewer components. Various components of the illustrated systems canbe implemented in one or more virtual machines, rather than in dedicatedcomputer hardware systems and/or computing devices. Likewise, the datarepositories shown can represent physical and/or logical data storage,including, e.g., storage area networks or other distributed storagesystems. Moreover, in some embodiments the connections between thecomponents shown represent possible paths of data flow, rather thanactual connections between hardware. While some examples of possibleconnections are shown, any of the subset of the components shown cancommunicate with any other subset of components in variousimplementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the invention can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further implementations of theinvention. These and other changes can be made to the invention in lightof the above Detailed Description. While the above description describescertain examples of the invention, and describes the best modecontemplated, no matter how detailed the above appears in text, theinvention can be practiced in many ways. Details of the system may varyconsiderably in its specific implementation, while still beingencompassed by the invention disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the invention should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the invention with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the invention to the specific examplesdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe invention encompasses not only the disclosed examples, but also allequivalent ways of practicing or implementing the invention under theclaims.

To reduce the number of claims, certain aspects of the invention arepresented below in certain claim forms, but the applicant contemplatesother aspects of the invention in any number of claim forms. Forexample, while only one aspect of the invention is recited as ameans-plus-function claim under 35 U.S.C sec. 112(f) (AIA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

1. (canceled)
 2. A networked information management system comprising:one or more computing devices; a client computing device associated witha storage policy, wherein the client computing device is configured withcomputer-executable instructions that, when executed, cause the clientcomputing device to: obtain, from the one or more computing devices, amachine learning (ML) model trained using data object data associatedwith the client computing device; and prioritize the ML model over thestorage policy to determine which data object to store in a secondarystorage device.
 3. The networked information management system of claim2, wherein second computer-executable instructions, when executed, causethe one or more computing devices to: retrieve user directoryinformation from a user directory system, wherein the user directoryinformation comprises an indication of an active or inactive status ofone or more user credentials; and train the ML model using the dataobject data and the user directory information.
 4. The networkedinformation management system of claim 2, wherein secondcomputer-executable instructions, when executed, cause the one or morecomputing devices to: retrieve second data object data associated withthe client computing device; retrain the ML model using the second dataobject data; and provide the client computing device with access to theretrained ML model.
 5. The networked information management system ofclaim 4, wherein the computer-executable instructions, when executed,further cause the client computing device to prioritize the retrained MLmodel over the ML model to determine which data object to store in thesecondary storage device at a future time.
 6. The networked informationmanagement system of claim 2, wherein the computer-executableinstructions, when executed, further cause the client computing deviceto generate a request to store in the secondary storage device a firstdata object in response to a prediction produced by the ML model,wherein the request causes the one or more computing devices to form asecondary copy of the first data object and store the secondary copy ofthe first data object in the secondary storage device.
 7. The networkedinformation management system of claim 2, wherein the ML model generatesa prediction, with an associated confidence level, identifying a firstdata object to store in the secondary storage device in response to oneor more inputs.
 8. The networked information management system of claim7, wherein the one or more inputs comprise at least one of a currenttime, an identification of a second data object generated by a firstapplication running on the client computing device, an age of the seconddata object, a name of the second data object, a size of the second dataobject, a data object type of the second data object, or informationidentifying an active or inactive status of one or more usercredentials.
 9. The networked information management system of claim 2,wherein second computer-executable instructions, when executed, causethe one or more computing devices to train the ML model by derivingpatterns from the data object data.
 10. The networked informationmanagement system of claim 2, wherein the data object data comprises atleast one of data object access times, data object permissions, dataobject ownership information, data object datapath information,information indicating which application running on the client computingdevice generated a data object, data object size, data object type, ordata object name information.
 11. The networked information managementsystem of claim 2, wherein the data object comprises at least one of afile, a folder, a directory, a file system volume, a data block, or anextent.
 12. The networked information management system of claim 2,wherein the computer-executable instructions, when executed, furthercause the client computing device to use the ML model and the storagepolicy to determine which data object to store in the secondary storagedevice if a prediction of the ML model does not conflict with an actiondefined by the storage policy.
 13. A computer-implemented methodcomprising: obtaining data object data associated with a clientcomputing device; obtaining, from one or more computing devices separatefrom the client computing device, a machine learning (ML) model trainedusing the data object data; and prioritizing the ML model over thestorage policy to determine which data object to store in a secondarystorage device.
 14. The computer-implemented method of claim 13, whereinthe one or more computing devices is configured to retrain the ML modelusing second data object data.
 15. The computer-implemented method ofclaim 14, further comprising prioritizing the retrained ML model overthe ML model to determine which data object to store in the secondarystorage device at a future time.
 16. The computer-implemented method ofclaim 13, further comprising generating a request to store in thesecondary storage device a first data object in response to a predictionproduced by the ML model, wherein the request causes the one or morecomputing devices to form a secondary copy of the first data object andstore the secondary copy of the first data object in the secondarystorage device.
 17. The computer-implemented method of claim 13, whereinthe ML model generates a prediction, with an associated confidencelevel, identifying a first data object to store in the secondary storagedevice in response to one or more inputs.
 18. The computer-implementedmethod of claim 17, wherein the one or more inputs comprise at least oneof a current time, an identification of a second data object generatedby a first application running on the client computing device, an age ofthe second data object, a name of the second data object, a size of thesecond data object, a data object type of the second data object, orinformation identifying an active or inactive status of one or more usercredentials.
 19. The computer-implemented method of claim 13, whereinthe data object data comprises at least one of data object access times,data object permissions, data object ownership information, data objectdatapath information, information indicating which application runningon the client computing device generated a data object, data objectsize, data object type, or data object name information.
 20. Thecomputer-implemented method of claim 13, wherein the data objectcomprises at least one of a file, a folder, a directory, a file systemvolume, a data block, or an extent.
 21. The computer-implemented methodof claim 13, further comprising using the ML model and the storagepolicy to determine which data object to store in the secondary storagedevice if a prediction of the ML model does not conflict with an actiondefined by the storage policy.